DRIVER BEHAVIOUR AND TRAINING
Human Factors in Road and Rail Transport Series Editors Dr Lisa Dorn Director of the Dr...
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DRIVER BEHAVIOUR AND TRAINING
Human Factors in Road and Rail Transport Series Editors Dr Lisa Dorn Director of the Driving Research Group, Department of Human Factors, Cranfield University Dr Gerald Matthews Professor of Psychology at the University of Cincinnati Dr Ian Glendon Associate Professor of Psychology at Griffith University, Queensland, and is president of the Division of Traffic and Transportation Psychology of the International Association of Applied Psychology
Today’s society must confront major land transport problems. The human and financial costs of vehicle accidents are increasing, with road traffic accidents predicted to become the third largest cause of death and injury across the world by 2020. Several social trends pose threats to safety, including increasing car ownership and traffic congestion, the increased complexity of the human-vehicle interface, the ageing of populations in the developed world, and a possible influx of young vehicle operators in the developing world. Ashgate’s ‘Human Factors in Road and Rail Transport’ series aims to make a timely contribution to these issues by focusing on the driver as a contributing causal agent in road and rail accidents. The series seeks to reflect the increasing demand for safe, efficient and economical land-based transport by reporting on the state-of-theart science that may be applied to reduce vehicle collisions, improve the usability of vehicles and enhance the operator’s wellbeing and satisfaction. It will do so by disseminating new theoretical and empirical research from specialists in the behavioural and allied disciplines, including traffic psychology, human factors and ergonomics. The series captures topics such as driver behaviour, driver training, in-vehicle technology, driver health and driver assessment. Specially commissioned works from internationally recognised experts in the field will provide authoritative accounts of the leading approaches to this significant real-world problem.
Driver Behaviour and Training Volume III
Edited by LISA DORN Cranfield University, UK
© Lisa Dorn 2008 All rights reserved. No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, mechanical, photocopying, recording or otherwise without the prior permission of the publisher. Lisa Dorn name has asserted her moral right under the Copyright, Designs and Patents Act, 1988, to be identified as the editor of this work. Published by Ashgate Publishing Limited Gower House Croft Road Aldershot Hampshire GU11 3HR England
Ashgate Publishing Company Suite 420 101 Cherry Street Burlington, VT 05401-4405 USA
Ashgate website: http://www.ashgate.com British Library Cataloguing in Publication Data International Conference on Driver Behaviour and Training (3rd : 2007 : Dublin) Driver behaviour and training. - (Human factors in road and rail transport) 1. Motor vehicle drivers - Training of - Congresses 2. Motor vehicle drivers - Attitudes - Congresses 3. Motor vehicle driving - Congresses I. Title II. Dorn, Lisa 629.2'83 Library of Congress Cataloging-in-Publication Data International Conference on Driver Behaviour and Training (1st : 2003 : Stratford-upon-Avon, England) Driver behaviour and training / edited by Lisa Dorn. p. cm. Includes bibliographical references and index. ISBN 978 0 7546 7203 6 1. Traffic safety--Congresses. 2. Automobile drivers-- Congresses. 3. Automobiles--Safety appliances--Congresses. 2. Automobile driver education--Congresses. 1. Dorn, Lisa. II. Title. HE5614.I553 2003 363.12'5--dc22
2003058287
ISBN 978-0-7546-7203-6
Printed and bound in Great Britain by MPG Books Ltd. Bodmin, Cornwall.
Contents List of Figures List of Tables Preface
ix xiii xvii
Part 1 The Novice Driver Problem 1
2
3
4
5
6
7
How Do ‘Significant Others’ Influence Young People’s Beliefs About Driving? Amanda Green and Lisa Dorn
3
Piloting a Telemetric Data Tracking System to Assess Post-training Real Driving Performance of Young Novice Drivers Robert B. Isler, Nicola J. Starkey, Peter Sheppard and Chris Yu
17
Fault Correction or Self-Assessment: Which Way Forward? Ian Edwards and Tracey Curle
31
New Elements in the Dutch Practical Driving Test: A Pilot Study Jan Vissers, Jolieke Mesken, Erik Roelofs and René Claesen
37
Personality and Attitudinal Predictors of Traffic Offences Among Young Drivers: A Prospective Analysis Lisa Wundersitz and Nicholas Burns
51
Pre-driving Attitudes and Non-driving Road User Behaviours: Does the Past Predict Future Driving Behaviour? Helen N. Mann and Mark J.M. Sullman
65
Prediction of Problem Driving Risk in Novice Drivers in Ontario: Part II Outcome at Two Years Laurence Jerome and Al Segal
75
Part 2 Emotions and Driver Behaviour 8
9
A Review of Studies on Emotions and Road User Behaviour Jolieke Mesken, Marjan Hagenzieker and Talib Rothengatter
91
A Comparison of the Propensity for Angry Driving Scale and the Short Driving Anger Scale Mark J.M. Sullman
107
vi
10
11
Driver Behaviour and Training – Volume III
Aggression and Non-aggression Amongst Six Types of Drivers Évelyne F. Vallières, Pierre McDuff, Robert J. Vallerand and Jacques Bergeron
117
The Influence of Age Differences on Coping Style and Driver Behaviour Elizabeth Andrews and Stephen Westerman
129
Part 3 At Work Road Safety 12
13
14
15
16
17
18
19
20
Effects of Organisational Safety Culture on Driver Behaviours and Accident Involvement Amongst Professional Drivers Bahar Öz and Timo Lajunen
143
Stages of Change in the Australian Workplace and its Application to Driver Education Tamara Banks, Jeremy Davey and H. Biggs
155
Prospective Relationships between Physical Activity, ‘Need for Recovery’ and Driver Accidents and Absenteeism Adrian Taylor and Lisa Dorn
167
Predicting High Risk Behaviours in a Fleet Setting: Implications and Difficulties Utilising Behaviour Measurement Tools Jeremy Davey, James Freeman and Darren Wishart
175
Driver Celeration Behaviour in Training and Regular Driving Anders af Wåhlberg and Lennart Melin
189
A Study of Contemporary Modifications to the Manchester Driver Behaviour Questionnaire for Organisational Fleet Settings James Freeman, Jeremy Davey and Darren Wishart
201
A Comparison of Seat Belt Use Between Work Time and Free Time Driving Among Turkish Taxi Drivers Özlem Şimşekoğlu and T. Lajunen
215
A Review of Developing and Implementing Australian Fleet Safety Interventions: A Case Study Approach Update Darren Wishart, Jeremy Davey and James Freeman
227
Designing a Psychometrically Based Self-Assessment to Address Fleet Driver Risk Lisa Dorn and Julie Gandolfi
235
Contents
vii
Part 4 Technological Interventions, Driver Behaviour and Road Safety 21
22
23
24
25
Development of Multimedia Tests for Responsive Driving Erik Roelofs, Marieke van Onna, Reinoud Nägele, Jolieke Mesken, Maria Kuiken and Esther Cozijnsen
251
The Effect of Simulation Training on Novice Driver Accident Rates R. Wade Allen, George D. Park and Marcia L. Cook
265
Driving Experience and Simulation of Accident Scenarios Catherine Berthelon, Claudine Nachtergaële and Isabelle Aillerie
277
Investigating the Contexts in which In-Vehicle Navigation System Users Have Received and Followed Inaccurate Route Guidance Instructions Nick Forbes and Gary Burnett
291
Comparison of Novice Drivers in Austria and the Czech Republic With and Without the Use of Intelligent Speed Adaptation Christine Turetschek and Ralf Risser
311
Part 5 Human Factors and the Road Environment 26
27
28
29
30
31
What Factors are Involved in Crashes, How Do We Measure Them and What Shall We Do About Them? Frank McKenna
325
Driver Training and Assessment: Implications of the Task-Difficulty Homeostasis Model Ray Fuller
337
Do We Really Drive by the Seat of Our Pants? Neale Kinnear, Steve Stradling and Cynthia McVey
349
The Impact of Subjective Factors on Driver Vigilance: A Driving Simulator Study Jérémy Vrignon, Andry Rakotonirainy, Dominique Gruyer and Guillaume Saint Pierre
367
The Use of Local Case Review Panels to Determine Contributory Factors Crash Data Peter Hillard, David Logan and Brian Fildes
379
The Effectiveness of New Seat Belt Legislation in Northern Ireland A.R. Woodside, J.R. Seymour and C. Gallagher
389
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Part 6 Rider Behaviour 32
33
34
Index
An Evaluation of the Portuguese Moped Rider Training Programme Patrícia António and M. Matos
399
Flow, Task Capability and Powered Two-Wheeler (PTW) Rider Training Paul Broughton
415
Understanding Inappropriate High Speed by Motorcyclists: A Qualitative Analysis Barbara Hannigan, Ray Fuller, H. Bates, Martin Gormley, Steve Stradling, Paul Broughton, Neale Kinnear and C. O’Dolan
425 443
List of Figures Figure 2.1 Figure 2.2
19
Figure 2.3 Figure 2.4 Figure 2.5 Figure 2.6
The telemetric data tracking system Mean responses and 95% confidence intervals of the participants in the driver training study (N = 36) for the questions: How safe do you feel driving: 1) at night? 2) in an unfamiliar area? 3) in the city? 4) in bad weather? 5) after drinking? 6) when sleepy or tired? 7) towing a trailer? 8) an unfamiliar car? 9) when angry? 10) when being tailgated? 11) at 100 km/h? 12) at 110 km/h? 13) at 120 km/h? The crashed car of participant #1 The map function of the on-line monitoring system Mean weekly maximum speeds for participants #1 and #2 Mean weekly maximum speeds for participants #3–#7
Figure 3.1
Miller and Stacey’s driving instruction learning curve
34
Figure 5.1
Distribution of the number of traffic offences recorded after questionnaire administration Flow chart of predictors of traffic offences for young drivers
Figure 5.2
Figure 11.1 Figure 11.2
Figure 14.1
Schematic of coping style and major associations with driving behaviour Interactions between: (i) age group and emotion-focused coping and (ii) age group and avoidance coping, for thrillseeking. Solid regression line indicates the younger group
21 22 23 26 26
55 59
134
135
The mediating effect of ‘need for recovery’ between physical activity (total MET-mins/week) and accidents and absenteeism (after three months)
171
Figure 16.1
Measurement method example
199
Figure 19.1
Conditions influencing driver behaviour
229
Figure 21.1 Figure 21.2
Processes involved in responding to interruptions An eclectic model for the assessment of driving competence
254
Simulator configurations and deployment milieu
266
Figure 22.1
258
x
Figure 22.2 Figure 22.3 Figure 22.4 Figure 22.5 Figure 22.6 Figure 22.7 Figure 22.8 Figure 23.1. Figure 23.2 Figure 23.3 Figure 23.4 Figure 23.5 Figure 23.6 Figure 23.7 Figure 23.8
Figure 24.1 Figure 24.2 Figure 24.3 Figure 24.4
Figure 25.1 Figure 25.2 Figure 25.3 Figure 25.4 Figure 25.5 Figure 25.6 Figure 25.7 Figure 25.8
Driver Behaviour and Training – Volume III
Subject population by age and gender Time distributions for subject licensure date Accidents relative to licensure Accident rates for each simulator configuration compared with previously published North American accident rates Number of subjects as a function of time beyond licensure Cumulative accident rate plots of simulator training groups as compared with rates from the literature Cumulative accident rate regression analysis trends The pedestrian shoots out from the right 2.4 seconds before the driver crosses his trajectory Average speeds and lateral positions as a function of time Average lateral positions as a function of time and driving experience Average speeds and lateral positions as a function of time Average speeds and lateral positions as a function of time Average TIVs as a function of time Average speeds and TIVs as a function of time Average lateral position as a function of time and driving experience Graph showing participants’ age distribution and gender (N = 712) Tree diagram highlighting contexts in which drivers have followed inaccurate route guidance instructions Reasons why participants have not updated the map on their navigation system (N = 498) Reasons why participants have updated the map on their navigation system (N = 374) Self-assessment and general feeling of safety on a five point scale (1 = ‘very safe’, 5 = ‘not safe at all’) Frequency of responses to the question: ‘What is a good driver in your opinion?’ Responses to the question: ‘What types of drivers endanger other road users? Responses concerning speed limits measured on a five-point-scale Awareness of ISA on a five-point scale Most frequent responses regarding advantages of ISA Most frequent responses concerning assumed disadvantages of ISA Willingness to use ISA (1 = ‘certainly not’, 5 = ‘yes, certainly’)
269 270 270 271 272 272 274
279 282 282 283 283 284 284 284
295 296 297 300
316 316 317 318 318 319 319 320
List of Figures
Figure 27.1 Figure 27.2
Figure 28.1 Figure 28.2 Figure 28.3 Figure 28.4 Figure 28.5 Figure 28.6 Figure 28.7
Figure 28.8
xi
Representation of the process of Task-Difficulty Homeostasis 338 Possible effects of increasing task demand (by increasing speed) to reverse a progressive decline in arousal (capability) 343 Authors’ illustration of the Task Capability Interface (TCI) Model (Fuller & Santos 2002) with influences Illustration of Task-Difficulty Homeostasis Illustrated summary of results from Fuller Means plot of task difficulty, feelings of risk and probability of collision across speed for the four road types Means plot of task difficulty ratings across speed for the four road types Means plot of feelings of risk ratings across speed for the four road types Magnified means plot of probability of loss of control ratings across speed for the four road types (ratings re-coded into 1–7 rating scheme) Maximum speed comparison by experience level on each road type
350 351 352 357 359 360
361 362
Figure 29.1 Figure 29.2 Figure 29.3
Road condition reproduced in the driving simulator Effects of subjective factors on performance metrics Interaction of age group and gender on sigma
Figure 30.1
Case study crash site before treatment identified by the local case review panel Case study crash site after treatment identified by the local case review panel
384
Figure 31.1 Figure 31.2 Figure 31.3 Figure 31.4
Do you ensure your child is restrained? Have you placed your child without restraining them? Have you purchased a booster seat? Do you feel the legislation will impact child safety?
392 393 394 395
Figure 33.1 Figure 33.2
Linear Model of Task Difficulty and Flow Outcomes of the dynamic interface between task demand and capability
417
Figure 30.2
Figure 34.1 Figure 34.2
Figure 34.3
Representation of the basic process of Task-Difficulty Homeostasis Representation of the process of Task-Difficulty Homeostasis, distinguishing between proximal (clear boxes) and distal (grey boxes) determinants and influences on compliance Schematic illustration of the research process
370 373 374
384
418
426
427 431
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List of Tables Table 2.1
Table 2.2
Table 4.1
Table 4.2
Table 5.1 Table 5.2
Table 5.3
Table 5.4
Table 7.1 Table 7.2 Table 7.3
Table 9.1 Table 9.2 Table 9.3 Table 9.4
The mean (M) weekly distance driven (Dist) in kilometres (km), number of trips (Trips) and mean speed per trip (Mean Speed) in kilometres (km/h) for seven of the eight participants. Standard Deviations (SD), minimum (Min) and maximum (Max) values are also given Weekly means of maximum speed in km/h (Max Speed), number of speeding violations per 100 km (Speeding Viol) and number of large g-forces per 100 km (G-force) for seven of the eight participants Perceived task difficulty of the four methods of independent driving (percentage responding that performance is ‘easy’ or ‘very easy’) Perceived task difficulty of the three categories of ‘productive’ special manoeuvres (percentage responding that performance is ‘easy’ or ‘very easy’) Background variables for young drivers recording and not recording a subsequent traffic offence Results of a linear regression predicting kilometres driven per year, using personality and attitudinal measures as predictors (N = 179) Mean scores on selected personality and attitudinal measures for drivers recording subsequent traffic offences and no subsequent traffic offences (N = 208) Results of logistic regression analysis for predicting at least one subsequent traffic offence, using personality and attitude measures as predictors (N = 179) Human factors predictors of problem driving events (a) Total driving events; (b) Collisions; (c) Violations Self-report predictors of problem driving events (a) Total driving incidents; (b) Collisions; (c) Violations Linear regression models of problem driving events (a)Total driving incidents; (b) Collisions; (c) Violations Alpha coefficients, means and standard deviations by gender Correlations between the main variables Predicting near misses Predicting violations
24
25
45
46
55
56
57
58
80 82 83 111 112 113 113
xiv
Table 10.1 Table 10.2 Table 10.3
Table 10.4
Table 12.1 Table 12.2 Table 12.3
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Mean standardised scores on the scales defining the young driver clusters Mean of reported verbal aggression, physical aggression and car using aggression Percentage of high speed driving, accidents, speeding tickets, enjoyment of driving and the mean of reported verbal and physical aggression and car-use aggression Attitudes, subjective norms, perceived control and intention to act aggressively in the intentional condition
122 123
124 125
Factor structure of the organisational safety culture scale Correlations between the study variables Regression of organisational safety culture on DBQ scales and accident involvement
150
The relationship between three levels of physical activity and ‘need for recovery’, health status, accidents and absenteeism
170
Table 15.1 Table 15.2
Alpha reliability coefficients of the measurement scales Logistic regression
180 182
Table 16.1
The percentage of men and people with Swedish names and mean age and number of hours worked in 2001, for drivers in the present study sample and for the total number of active drivers at Gamla Uppsalabuss as at 30 December 2001 The means and standard deviations of the celeration variables in m/s2 during the first (A), and second (B) runs during the training sessions, and the mean for regular driving along a route for (some of) the same drivers The N and t-values for the differences (dependent t-tests) # between different measurements of celeration behaviour. Numbers 5 and 6 were gathered after training (A and B) The Pearson correlations between driver celeration behaviour for the first and second run during training on one hand, and for regular driving on a number of occasions; 1 before training, 5 and 6 after
Table 14.1
Table 16.2
Table 16.3
Table 16.4
Table 17.1 Table 17.2 Table 17.3 Table 17.4 Table 18.1
Alpha reliability coefficients of the DBQ scale Mean scores for the DBQ factors Factor structure of the modified DBQ Logistic regression Mean response values for the reasons for not using a seat belt
147 148
193
193
194
194 205 206 208 209 218
List of Tables
Table 18.2
xv
Results of factor analysis for the items related to reasons for not using a seat belt when driving a taxi and a private car Results of factor analysis for the items of DSI Factors related to reported seat belt use frequency when driving a taxi Factors related to reported seat belt use frequency when driving a private car
222
Table 20.1 Table 20.2 Table 20.3 Table 20.4 Table 20.5 Table 20.6
Participant data Factor structure of Questionnaire A post-PCA Factor structure of Questionnaire B post-PCA Factor structure of Questionnaire C post-PCA Participant data Gender differences in FDRI factors
238 240 241 241 242 242
Table 22.1 Table 22.2
Subject population by simulator configuration and gender Accident rate regression analysis
269 273
Table 23.1
Average response times as a function of driving experience and scenario; standard deviations in parentheses Singular behaviours
281 286
Specific contexts and participants’ examples of occasions where they have received and followed inaccurate route guidance instructions
298
Table 18.3 Table 18.4 Table 18.5
Table 23.2 Table 24.1
Table 26.1 Table 26.2
Table 28.1 Table 28.2 Table 28.3
Table 28.4
Table 29.1 Table 29.2
The intercorrelation of speed, close following, alcohol and violations The correlations between the two potential underlying factors (thrill and emotional outlet) and the risk factors Speed range across the different road types (clips set at 5 mph increments) Breakdown of sample by experience and gender Correlation coefficients of task difficulty and feelings of risk ratings by speed and road type (all coefficients significant at the p < 0.001 level) Mean speeds in mph for ratings threshold and maximum speed by road type (correlation coefficient between the two variables also shown: p = ns for all road types) Expected impact of low vigilance on performance metrics Impact of extraversion on performance metrics
219 220 221
331 331
354 355
358
358 371 372
xvi
Table 30.1
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Frequency of occurrence of contributory factors across the 79 crashes reviewed
385
Table 31.1
Attitudes of drivers towards seat belt legislation
394
Table 32.1 Table 32.2 Table 32.3
t-test results for public riding behaviour and traffic offences 403 t-test results for rider equipment and safety accessories 404 t-test results on physical and psychological features while riding (internal risk factors) 405 Descriptive statistics for crash experience (moped/motorcycle and car) between 2000 and 2003 405 Odds ratios (95 per cent CIs) for moped/motorcycle crash experience as dependent variable 407
Table 32.4 Table 32.5
Table 33.1
The four states of flow
417
Table 34.1
Broad themes discussed during the focus group
430
Preface Working in road safety is not an easy path to tread. There is a climate of poor funding, turgid political will and an almost tacit societal indifference towards the annual carnage on the world’s roads. Yet there are two important (and tenacious) professional groups without which this state of affairs would only worsen – researchers and practitioners. The International Conference in Driver Behaviour and Training unites these two groups to debate some of the latest research on how to improve road safety. In recent times, there has been an impressive increase in research in the field of driver behaviour and training, particularly in our understanding of the effects of graduated driver licensing and the potential benefits of incorporating the Goals for Driver Education model into the driver education process. In the UK, a new strategy for the training and testing of drivers is about to be announced. There are also exciting developments in the use of new technologies and innovative solutions, with more and more companies taking a keen interest to reduce the risk of work-related crashes such as with the using of simulator-based driver training for example. I introduced the conference in 2003 because there seemed to be little opportunity for researchers and practitioners to meet and debate these kinds of topics, especially in an international forum. Perhaps this is because their motivations and approaches to improving road safety differ. Practitioners are used to dealing with real world problems and finding solutions. Researchers, on the other hand, are motivated by the need to contribute to knowledge in the field and advance their career through research funding and scientific publications. To work with practitioners is not a recognised academic activity. The major fallout here is that there is a growing scientific base of research on driver behaviour, education and training – yet custom and practice methods flourish. The research findings don’t seem to be getting through at ground level. Policy makers are justifiably reluctant to support interventions without research evidence, leading to a slow uptake of potential solutions that could save lives. There are many methodological problems that researchers have to tackle in conducting research in the road safety field that a practitioner may not be aware of – such as the incredibly difficult process of interpreting crash data or understanding individual differences in response to interventions. In addition, collecting and analysing data is an expensive and time consuming business and research is somewhat slow and drawn out – a source of frustration for policy makers. Worse still, research just generates more questions. The complexity of the issues means that researchers may sometimes fail to communicate theories and models in a way that practitioners and policy makers can interpret and put into practice. As if I didn’t need reminding of the importance of this field of enquiry, I lost a dear cousin, Colin Bailey, in a road traffic accident only last year. His death continues to bring untold distress to my family. It also brought a renewed sense of urgency to me. If we are to succeed in designing effective road safety interventions, both
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professional groups need to collaborate and coordinate far better if we are going to make real progress across the world. This conference is just one opportunity to work together to integrate our understanding of how to improve a driver’s decision-making in the dynamic and complex process of being a road user; more importantly, which interventions deliver the most benefit. I hope that the Third International Conference in Driver Behaviour and Training helps to align our motivations, maintain the momentum, extend the research foundation and consider the evidence on the best way to train and educate drivers. My gratitude goes to Cranfield University for financing the event, a2om for their sponsorship, authors and speakers for their excellent contributions. The contents of this book are a testament to a mere selection of some of the excellent research taking place across the world – but more needs to be done to get researchers and practitioners together to implement interventions that have been demonstrated to work under the scrutiny of an unbiased peer-reviewed process.
PART 1 The Novice Driver Problem
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Chapter 1
How Do ‘Significant Others’ Influence Young People’s Beliefs About Driving? Amanda Green and Lisa Dorn Cranfield University, UK
Introduction Road safety professionals are keen to understand what causes young people to develop risky driving styles. It has long been known that we are drawn to behave in ways that are consistent with our beliefs. Given the well documented role of parents in the development of young people’s ways of thinking, one avenue of research has been to consider how parents influence their children’s future driving. Early research found a positive correlation between fathers’ and sons’ convictions (Carlson and Klein, 1970), supported by more recent studies showing associations between parents’ and offsprings’ speeding behaviour (Fleiter, Watson and Lennon, 2006; Bianchi and Summala, 2004), aggression and drink driving (Gulliver and Begg, 2004; Mulvihill, Senserrick and Haworth, 2005; Ferguson et al., 2001) and crash risk (Ferguson, Williams Chaplaine, Reinfurt and DeLeonardis, 2001). Risk taking amongst young people is also dependent on family structure, normative parental influences and social influences (Shope, Waller, Trivellore, Raghunathan and Patil, 2001; Shope, Ragunathan and Patil, 2003). The presence of other people in the car with the driver has also received some attention with both age and sex of passengers being differentially related to crash risk. For age, Regan and Mitsopoulos (2001) found a higher crash risk when teenage passengers were present, whereas when driving with an older adult or a child, young people’s crash risk was reduced. For sex, an OECD (2006) report showed that young drivers’ crash risk is significantly increased by the presence of similarly aged passengers, particularly if both the driver and passengers are male. These two research enquiries suggest that young people’s driving behaviour may be generated through social learning processes, specifically in their experiences of the person-environment interactions of driving by ‘significant others’ (defined as parents, relatives and friends: see Bandura and Walters, 1963; Bandura, 1997; Mishel, 1999; Matthews, Deary and Whiteman, 2003), but the research tends to consider correlations between the driving behaviour of young drivers and their friends and relatives without reference to what may be the cause of this relationship. Little is known about the development of driving-specific characteristics and situational learning (Lave and Wenger, 1991) or how young drivers are influenced by significant others.
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All learning is contextual and embedded within a social and physical environment (Hampson, 1988 cited by Matthews et al., 2003). It could be that driver beliefs, feelings and behaviour are a learned response according to the cues provided by others. Understanding the social processes by which these cues are internalised during adolescence and expressed when a young person starts driving will enable road safety professionals to focus educational interventions more effectively towards the development of safer driving styles. Understanding the mechanisms by which young drivers are influenced by significant others’ behaviour and the social factors at play is an important step to identifying what underlies the development of their beliefs about driving. Social cognition is the study of how people process social information, especially its encoding, storage, retrieval and application to social situations. Social norms are developed through interactions with peers and parents and affect future decisionmaking and these processes have been studied in the context of driving (Victoir, Eertmans, Van den Bergh and Van den Brouke, 2005). Forward (2006) conducted a qualitative study to investigate drivers’ intentions to commit violations and found that ‘violators’ tended to believe that others would approve of their driving actions. They also tended to normalise their driving behaviour by using examples of others’ driving practices and arguing that their risky behaviour was therefore fairly common. The present study is an extension of Forward’s (2006) work, but adopts a social learning approach by considering the importance of observational learning and modelling of the attitudes, behaviours and emotional reactions in the driving of significant others (Bandura, 1977). In other words, we anticipate that driving styles are learned through our observations of significant others’ behaviours. We expect that young people may have learned both desirable or detrimental driving styles and attitudes depending on the behaviour of their role models. Further, we expect issues of self-image and identity will be of paramount importance in the way they respond and adapt to societal norms and expectations set by significant others (Christmas, 2007). Methodologically, previous research has focused on the extent of the associations between significant others and young people on violations, crash rates and other driving behaviours. This approach says little about the nature of young people’s driving-related experiences, nor how the driving of friends and relatives is viewed. The present study uses a qualitative analysis to explore these experiences in depth allowing young people to articulate their driving-related experiences and how they think this has affected their beliefs and behavioural intentions as a driver. The study was commissioned by a2om Ltd, a new driving academy that aims to provide a comprehensive education for learner drivers. Method An in-depth exploration of young learner drivers’ beliefs about driving using a semistructured interview method was used. This allowed a free flowing discussion on the topic areas of interest using focus group methodology. This method was considered the most useful, as a number of different perspectives could be collected during the
How Do ‘Significant Others’ Influence Young People’s Beliefs About Driving
5
interview, allowing for the viewpoints of a greater number of participants in the time allotted. A fundamental framework was used to inform the topic areas for the focus groups after a pilot study revealed its utility. The framework was constructed to elicit accounts about what young people think about the driving of significant others, with a view to understanding how these experiences have led to the development of their beliefs about how they will drive in the future. Participants A total of 65 young people took part in 16 focus groups. The focus group sizes ranged from between three and six participants. The participants were selected from several different educational establishments across the UK. Eight schools and colleges took part from both the independent and public school sector. The schools included one mixed independent school, two mixed comprehensive schools, one all girls independent school, two all boys independent schools and two mixed state sixth form colleges. Two focus groups were conducted at each location. All participants volunteered to take part in the research. Participants were advised of the full nature of the research and advised of their right to withdraw from the study at any time. Confidentiality and anonymity were also assured. Participants were aged between 17 and 19 years and included 43 males and 22 females. Twenty-nine participants had no driving experience, three had had no lessons but some driving experience, 23 were currently having driving lessons, with six of those having extra supervised practice as well, two were learning with a parent and two had passed the theory test. Procedure After introductions and ice breakers, initial questions relating to whether the participants were currently learning to drive provided an opportunity for a rapport to be established between the interviewer and interviewees. This also helped to familiarise participants with the recording equipment to help put them at ease. The focus group duration ranged between one and one and-a-half hours with a mean time of 1:04 hours. The interviews were recorded and fully transcribed. Analyses A template analysis approach to coding was utilised to reveal and organise themes relating to teenagers’ perceptions about driving that were in line with the research objectives. An initial template was constructed by systematically analysing the transcripts to identify relevant sections of text (King cited in Cassell and Symon, 2004). The pre-defined codes helped to guide the analysis but these were kept to a minimum so as not to blinker identification of further emergent themes and subthemes. Preliminary codes, along with corresponding quotations, were entered into Excel in order to manage the plethora of data generated. The data were reanalysed and a tentative template was generated. Further analysis and interpretation in relation to the project aims resulted in a simplified final template. This consisted of two
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highest-order codes (themes), which were then sub-divided into lower-order codes (sub-themes). Results and discussion This study reports two main themes that help to explain how young people interpret their driving-related experiences as pre-drivers and part of fuller analyses that will be reported elsewhere. The two broad themes to emerge were firstly ‘modelling others’ driving behaviour’ and secondly ‘distancing from others’ driving behaviour’. Taking each of these themes in turn, the sub-themes will be discussed in relation to the data to derive a possible explanation for the mechanisms through which young people develop their beliefs about driving. Theme 1: modelling others’ driving behaviour The first theme to emerge was concerned with how young people make sense of their experiences of the driving of significant others, in particular, whether there was evidence of modelling, defined as viewing the driving of significant others as desirable, and whether this could be considered safe or not. Driving aspiration Respondents were asked, ‘Once you pass your test, what kind of driver would you like to be?’. Approximately 65 per cent of the responses referred to the desire to be patient, cautious or safe drivers. Many stated that they would want to drive in a controlled fashion and be able to handle the car in any eventuality: A controlled driver who knows the boundaries so he knows what’s coming or he knows if he is going too fast or can control his speed or is just generally aware of everything (Gp12, Independent co-education school).
But as the interviews progressed, it was clear that our participants held some contradictory beliefs with respect to speeding in particular. Modelling fathers About 20 per cent of the sample intended to imitate their fathers’ driving styles. Yet 17 teenagers described fathers as regularly exceeding the speed limit. Some of the descriptions sounded as though their father’s driving was considered somewhat heroic – hence a plausible explanation as to why their driving was considered worthy of emulating. Always in control … even though he was driving fast I would still feel safe. I feel safe when I am in a car with him (Gp3, All boys independent school).
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You do pick up their habits. I’ve noticed that I do similar things to my dad more when my dad does something than my mum does; I have picked up some of his habits … mainly with speeding, over the limit, I drive on the limit sort of thing (Gp3, All boys independent school). Dad, he is so confident behind the wheel; he is really macho as well, he is quite macho about his car so at the same time he is quite confident. He is definitely a role model (Gp11, Independent co-education school).
Further exploration revealed that a good driver was perceived as someone who could control a car, but not necessarily within the speed limits. Driving at speed was felt to be relatively safe: Risky but safe like you’re safe but I wouldn’t be afraid to go a little bit faster. If you’re on a motorway and it’s like a clear road and I wouldn’t go absolutely crazy, as long as you’re being safe (Gp4, All Boys Independent School).
Similarly, many participants described their mothers as being ‘good drivers’; but at the same time they recalled experiences where their mothers were clearly lacking awareness or concentration and their father’s driving was held to be superior in comparison: I think my dad, well I feel more confident with my dad’s driving, although he does tend to go quite fast and he sometimes drives with his knees. My mum you have to be alert because otherwise it’s very embarrassing. She might go ‘oh my god I need to go to …’ and she just stops in the middle of the road (Gp10, All boys independent school). My mum does her make up and stuff and she is always on the phone. I don’t have to concentrate when my dad’s driving but when my mum is driving, the lights will change or something and she won’t notice because she’s sat there looking in the mirror or something (Gp10, All boys independent school).
Fathers’ driving appears to be held in particularly high esteem, with both teenage girls and boys preferring to model their father’s driving style rather than their mother’s. I think my dad is a brilliant driver and my mum’s a crap driver. It’s fun being in the car with him, it’s like being with a 20 year old driving along. He is really, really cocky but he thinks he is really good. He slows down when he sees something you know and he knows what he is doing. My mum just screams every two seconds and crashes into everything so I don’t think she should have passed her test (Gp12, Independent co-education school).
Our pre-drivers believed that confidence is an important factor in driving. Those who were confident that they can take on their father’s driving style were more likely to dismiss thoughts of safety and expressed a desire to take on more challenges. Speeding sometimes if it’s like a quiet road like back lanes and stuff (Gp6, Co-education sixth form college).
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The general consensus was that speeding was acceptable providing there was an empty road, limits were not exceeded by too much and that they felt able to control the car. Perhaps the associations between parents’ and children’s speeding behaviour reported by Fleiter et al. (2006) and Meadows, Stradling and Lawson (1998) may be largely attributed to the modelling of fathers’ driving behaviour rather than mothers’. There was evidence of a normative framework in operation according to some of the narratives. Young people felt that their mother and father had a shared understanding of how to behave towards other drivers and they worked together to make sure other drivers understood their feelings, confirming previous research (Pelsmacker and Janssens, 2007; Tabman-Ben-Ari, 2006). My dad like at the lights he gets aggressive, not road rage but he just gets aggressive with other drivers if they are going too slow … he calls them a ******. And when my mum sits in the passenger seat she sometimes, if my dad if he is going too fast, if my dad can’t stick his finger up, my mum does it for him (Gp5, Co-education sixth form college).
Friends or siblings as role models Some of the participants were influenced by the driving styles of similarly aged friends and siblings as well as parents. Some positively … One of my true best friends, who is a couple of years older, she takes me out in her car sometimes and she is really, really confident and relaxed behind the wheel and just takes everything in her stride and I would quite like to be like that (Gp1, All girls independent school).
And some negatively … My brother passed his test and then he went a cruise one night and there were three other cars as well, like his friends. We were in the second car and we went round the corner and everyone was going really fast. We went round and the first car went into a bridge and then my brother went into him and then they all went into each other. The first car the guy had to be cut out of it … it’s actually alright it doesn’t bother me at all (Gp12, Independent co-education school).
Night cruises (many cars congregating in one area and driving competitively for fun, usually late at night) is a worrying trend amongst young people. Young drivers often believe that positive outcomes outweigh any negative consequences to their actions (Clarke et al., 2006; Fromme, Katz and Rivet, 1997) acting for the fun of the moment and feeling invulnerable. Carmichael et al., (2005) found that teenagers who reported that their peers were involved in delinquent behaviours had lower perceptions of sanction and were more likely to violate; judging their friends’ behaviour as acceptable helps them become part of the ‘in-group’.
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It’s great fun going in your friend’s car and yes I definitely want to do it (Gp1, All girls independent school). I would like to be safe but silly. Not racing but, it’s hard to explain I guess but just have a bit of fun, being serious but at the same time fun (Gp5, Co-education sixth form college). The friends that I have drive and they go to cruises at night where there is about 25 cars. We was driving up and there was like a slip road and one of his friends just suddenly pulled out right onto us and we had to try and swerve but we couldn’t go right because there was another car there so we had to go into a lamp post and the car was really smoking and everything (Gp5, Co-education sixth form college).
There is also some evidence of individual differences in sensation seeking. I kind of enjoy it more because it’s more fun because I’ve got a mad edge (Gp10, All boys independent school).
Drink driving experiences Analysis revealed over 40 references to drink driving experiences. Nine teenagers stated that they had one or more parents that regularly drink drive and this has influenced their future behaviour. Some experiences had not resulted in any punishment or crashes and this served to reinforce poor learning: If I had never driven before I would never have drank anything before getting into a car; I would never do it but my mum, she always drinks and drives. She doesn’t drink a lot but she’ll go over the legal amount all the time and she still drives. She hasn’t had any major crashes that were her fault so I wouldn’t mind having a bit, but I wouldn’t get drunk (Gp4, All boys independent school).
Whereas others had experienced negative outcomes: A lot of people I know, like a lot of my family have all been in like drinking accidents. My brother I think he crashed a car when he was drinking and my dad got pulled over when he was a lot younger but he had loads of people with them as well so (Gp6, Co-education sixth form college).
Narratives show that many parents suffered no serious consequences of drink driving, leading to the obvious conclusion that young people are learning that it’s safe to drink and drive. Even though most teenagers report that they would not drink and drive themselves, previous research suggests that parents’ driving records are often predictive of their children’s driving records, as cited earlier. In conclusion, we find evidence for strong modelling effects of unsafe driving practises emanating both from parents and peers.
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Theme 2: distancing from others’ driving behaviour The second major theme to emerge referred to beliefs about the kinds of drivers young people want to avoid turning out like, such as those who have hesitant or reckless driving styles. In other words, the kinds of driver they want to distance themselves from. Observing others: how not to drive Several accounts suggest that participants had internalised the criticisms levied at their significant others or had observed their driving and decided they wanted to be a more skilled driver than the example set: My dad’s quite stop start and everyone gets quite car sick with him … I want to be able to drive smoothly and right now when I brake I know I brake quite jerky and it’s not a smooth stop. So I want to be a smoother driver (Gp3, All boys independent school).
Some participants described their grandparents’ driving as ‘scary’ and felt anxious when in a car with them. Their grandparent’s driving was judged to be slow and hazardous and respondents wanted to distance themselves from becoming this kind of driver. Fourteen participants said they find old or slow drivers frustrating and expect to feel resentful if, as a driver in the future, they are delayed by them. Sometimes you get old people on the road and they are going really slowly and people get really annoyed with them and try and over take them when they shouldn’t really over take them and then you get quite a lot of accidents (Gp10, All boys independent school).
They also distanced themselves from significant others who had a nervous or hesitant driving style. I hate being in a car with my sister because she’s quite nervous and she’s really putting off the fact that she’s nervous about her driving which makes me nervous, is somewhat annoying. So I’d like to be quite confident and I would like to be seen, I mean my sister’s quite nervous of driving in front of other car users so I would like people to feel quite confident with me (Gp4, All boys independent school).
The distancing process is entirely consistent with the previous theme in which teenagers show a preference for calm, confident driving styles. Teenagers do not want to be associated with driving that will invoke a negative reaction from other drivers, presumably to protect their self-identify and self-esteem. Their preferred driving style will deliver a major psychological benefit – allowing them to feel superior in their driving skill compared with these ‘lesser’ drivers. However, teenagers seem to draw a line between the slow, bumbling or nervous driver and those with an aggressive driving style – being equally committed to not adopting either driving style. I have a brother who drives and I find that he can get aggressive pretty quickly. Either being impatient when traffic is moving slowly or when he wants to pull out of somewhere
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and he is there ages, waiting to go … he will sit there and just look and it’s his tone of voice like ‘hurry up, come on’ you know. Copy his driving style? No. I’d like to be a calm driver and have patience for other people and let people turn out if they want, just a patient driver really (Gp6, Co-education sixth form college).
It seems that some young people are keen to avoid the pain of being thought of as a slow, nervous or aggressive driver and instead seek the pleasure of being thought of as a skilled, confident driver; but this is largely dependent on the way they have interpreted their experiences. Our data are consistent with the findings of a recent Department for Transport study (Christmas, 2007) showing that young drivers considered good driving to be largely a function of ‘natural talent’. In this study, some young people were found to be highly confident in their driving ability, choosing to ignore caution from others with image and identity and the opinions of others being important to them. We can also confirm that these beliefs and personal needs are present before they even pass their driving test according to the present data. Peer pressure influence on driving behaviour Mainstream research shows the strong effects of peer pressure on young people (Brown, 2004). With regards to driving though, many respondents considered that passengers could influence their driving, but felt they could handle these difficult situations and would not concede to peer pressure: There are people who wouldn’t encourage me to do anything and then there are people I am sure would. I doubt, I don’t know it depends on the situation at the time, but I doubt I would actually put my foot down just because I was told to. I’m not likely to be taken to peer pressure so … not really a problem (Gp14, co-education college). I think it would be tempting but I wouldn’t do it because I know the risks. I would just turn around and say no. They are the passenger so it’s your choice, it’s not like they are going to jump in the front and drive (Gp6, Co-education sixth form college).
We propose that it is not socially desirable to admit to being influenced by peers as this would conflict with their image of being a skilled, confident driver in complete control as outlined in the modelling theme. They prefer to be seen as independent and above the influence of others. Depending on the situation like, if it’s a busy road or if it’s dark or icy then there’s more chance and I would say ‘look guys would you mind calming down because it’s difficult enough to drive as it is’, but if it’s a clear enough road, I’d probably be joining in. Not like leaning over hitting people but saying things and laughing as well (Gp3, All boys independent school). You don’t mean to but I reckon that you do. Subconsciously there is something that makes someone drive faster than they would do, I reckon, than on their own (Gp3, All boys independent school).
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Perhaps it is the mere presence of peers that influences driving behaviour rather than any active encouragement from friends to take risks (Regan and Mitsopoulos 2001; Waylen and McKenna, 2002). Some accounts suggest that they would be too concerned for the welfare of their passengers to take risks: There is a difference in driving and something that you do yourself because if you do have a car full of people and you are putting lives at risk whereas if you are on your own it will just be your own life (Gp6, Co-education sixth form college). Because you are responsible and you have got that responsibility, especially when your friends are there, they won’t want to get into any trouble (Gp1, All girls independent school). I want a two-seater I don’t want to have to drive people and if I have an accident I will kill less people (laughs) not that I will you know but just in case (Gp1, All girls independent school). I think you have a responsibility to take care of everyone in the car and ensure that they are not a distraction … try to ignore what they are saying (Gp11, Independent co-education school).
These findings support those reported by Glendon (2005) who also found that young drivers are reluctant to put their passengers at risk. Narratives suggest that on the one hand participants consider speeding is safe, yet on the other hand are concerned for the safety of their passengers. Perhaps they reconcile this apparent inconsistency in beliefs by believing that they will be skilled enough to exceed the speed limit and still be safe. Early experiences and effects on future driving styles One salient sub-theme related to memorable early experiences: I saw, when I was nine, I was walking through, not my village but another village fairly close. I was with a friend and there was a crossroads in the village and it was on quite a steep hill. A guy on a moped came charging down a hill and tried to turn. Obviously he hadn’t been in the village much at all before; he had no clue. He just came down and skidded and went straight across, hit the curb and bounced up into a wall on the side of the road and just like lay there unconscious. My friend stayed with the guy and I legged it and called an ambulance. That completely put me off bikes (Gp9, All boys independent school).
Our data support the view that beliefs about driving are probably developed much earlier on in life than expected, perhaps because children have more exposure to driving experiences than ever before (Waylen and McKenna, 2002). Some extreme personal experiences have a lasting effect on perception of risk and leads to beliefs that will distance them from situations that could place them in danger.
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My dad was in hospital for quite a long time, and there was loads of people there who were like paralysed for the rest of their life because someone else was drunk when they were driving and it has completely altered their life really. I wouldn’t drink and drive or get in a car when somebody had been drinking (Gp10, All boys independent school).
Conclusions Somewhere along life’s highway, young people have developed quite sophisticated views about driving and the kind of driver they would like to be before they even take to the road. Teenagers are responding and adapting to societal norms and expectations learned through their experiences of the driving of significant others. They do this in two ways. Firstly, young people model their driving on friends and relatives that speed or drink and drive and consequently develop the view that this behaviour is safe when not done to excess. They also learn how they should respond to road users who drive slower or more cautiously. There was a strong association between skill and speed, and the general consensus of a good driver emerged as someone who can drive at speed while still maintaining control of the vehicle, with fathers being the main source of their observational learning. Secondly, they develop their driving styles through a process of distancing themselves from those with a hesitant and overly aggressive approach to driving. Their experiences tell them that this will attract criticism from other road users. The data also suggest strong indications of individual differences in the ways that experiences are interpreted by young people. Underpinning interpretation of their driving experiences is the desire to avoid the pain of criticism from others, supporting the view that self-image and identity are critical to young people. Our study shows the need to be regarded as a good driver is primarily motivated by the need to enhance self-esteem about their driving skill. Low parental monitoring has been related to risky driving behaviour, traffic violations and young driver crashes (Hartos, Eitel, Haynie and Simons-Morton, 2000). Parents are in the best position to enforce driving restrictions for learners and to instil safe behaviour (Mulvihill, Senserrick and Haworth, 2005), but judging by the reports of their driving behaviour many parents are setting a poor example. Educating parents may be the first step towards ensuring that young people avoid acting out the dangerous behaviours they have witnessed as an inexperienced driver. We consider that encouraging self-reflection on these formative experiences will facilitate a less distorted interpretation of their experiences and what constitutes safe driving provided this is under the guidance of a trained professional. Changing attitudes does not require deep psychological therapy; rather we only need to create doubt in the certainties that are held. Driving instructors at Cranfield University are currently being trained in techniques to challenge beliefs and provide a more focused education tailored to the learners particular risk profile (Dorn, 2005). A new breed of driving instructor delivering a comprehensive curriculum to educate drivers, not just in how to drive a vehicle but in their personal risks as well, has already begun with a2om.
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Driving instructors need some way of assessing individual differences to tailor their training. An instrument for self-assessment has been developed as part of the present research programme as a first step to facilitate self-reflection on personal motives, beliefs, feelings and goals and how these individual differences might impact on future driving styles in line with the Goals for Driver Education matrix (Hatakka et al., 2002). References Bandura, A. (1977). Social Learning Theory. New York: General Learning Press. Cited at http://tip.psychology.org/bandura.html. Bandura, A. (1997). Self-Efficacy: The Exercise of Control. New York: W.H. Freeman and Co. Bandura, A. and Walters, R.H. (1963). Social Learning and Personality Development. New York: Holt Rinehart and Winston. Bianchi, A. and Summala, H. (2004). ‘The “genetics” of driver behaviour: parents’ driving style predicts their children’s driving style.’ Accident Analysis and Prevention, 36, 655–9. Brown, B. (2004). ‘Adolescents’ relationships with peers.’ In R. Lerner and L. Steinberg (eds). Handbook of Adolescent Psychology. New York: Wiley and Sons. Burns, P.C. and Wilde, G.J.S. (1995). ‘Risk taking in male taxi drivers: relationships among personality, observational data and driver records.’ Personality and Individual Differences, 18, 267–78. Carlson, W.L. and Klein, D. (1970). ‘Familial vs. institutional socialization of the young traffic offender.’ J. Saf. Res. 2(1), 13–25 cited in Bianchi, A. and Summala, H. (2004). ‘The “genetics” of driver behaviour: parents’ driving style predicts their children’s driving style.’ Accident Analysis and Prevention, 36, 655–9. Carmichael, S., Langton, L., Pendell, G., Tritzel J.D. and Piquero A.R. (2005). ‘Do the experiential and deterrent effect operate differently across gender?’ Journal of Criminal Justice, 33, 267–76. Cassell, C. and Symon, G. (2004). Essential Guide to Qualitative Methods in Organizational Research. London: Sage Publications. Christmas, S. (2007). ‘The good, the bad and the talented.’ Road Safety Research Report No. 74. Department for Transport. Wetherby: DfT Publications. Clarke, D.D., Ward, P., Bartle, C. and Truman, W. (2006). ‘Young driver accidents in the UK: the influence of age, experience and time of day.’ Accident Analysis and Prevention, 38, 871–8. Clarke, D.D., Ward, P. and Truman, W. (2005). ‘Voluntary risk taking and skill deficits in young driver accidents in the UK.’ Accident Analysis and Prevention, 37, 523–9. Dahlen, E.R. and White, R.P. (2006). ‘The big five factors, sensation seeking and driving anger in the prediction of unsafe driving.’ Personality and Individual Differences, 41, 902–15. Dorn, L. (2005). ‘Driver Coaching.’ In L. Dorn (ed.) Driver Behaviour and Training,
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vol. 2. Aldershot: Ashgate. Ferguson, S.A., Williams, A.F., Chaplaine, J.F., Reinfurt, D.W. and DeLeonardis, D.M. (2001). ‘Relationship of parent driving records to the driving records of their children.’ Accident Analysis and Prevention, 33, 229–34. Fleiter, J., Watson, B., Lennon, A. and Lewis, I. (2006). ‘Significant others, who are they? – examining normative influences on speeding.’ In Proceedings 2006 Australasian Road Safety Research Policing Education Conference, Gold Coast. Forward, S.E. (2006). ‘The intention to commit driving violations: a qualitative study.’ Transportation Research Part F 9, 412–26. Fromme, K., Katz, E.C. and Rivet, K. (1997). ‘Outcome expectancies and risk-taking behaviour.’ Cognitive Therapy and Research, 21(4), 421–43. Glendon, A.I. (2005). ‘Young drivers’ attitudes towards risks arising from hazardous driving behaviours.’ In L. Dorn. (2005). Driver Behaviour and Training, vol. 2. Aldershot: Ashgate. Gulliver, P. and Begg, D. (2004). ‘Influences during adolescence on perceptions and behaviour related to alcohol use and unsafe driving as young adults.’ Accident Analysis and Prevention 36, 773–81. Hartos, J.L., Eitel, P., Haynie, D.L. and Simons-Morton, B.G. (2000). ‘Can I take the car?: relations among parenting practices and adolescent problem-driving practices.’ Journal of Adolescent Research, 15(3), 352–67. Hatakka, M., Keskinen, E., Gregersen, N.P., Glad, A., and Hernetkoski, K. (2002). ‘From control of the vehicle to personal self-control: broadening the perspectives to driver education.’ Transportation Research Part F: Traffic Psychology and Behaviour, 5, 201–15. Lave, J. and Wenger, E. (1991). ‘Situated learning: legitimate peripheral participation.’ Cambridge: University of Cambridge Press. Lerner, R and Steinberg, L. (eds) (2004). Handbook of Adolescent Psychology. New York: Wiley and Sons. Matthews, G., Deary, I.J. and Whiteman, M.C. (2003). Personality Traits. Cambridge: Cambridge University Press. Meadows, M.L., Stradling, S.G. and Lawson, S. (1998). ‘The role of social deviance and violations in predicting road traffic accidents in a sample of young offenders.’ British Journal of Psychology, 89, 417–31. Mishel, W. (1999). ‘Personality coherence and dispositions in a cognitive-affective personality (CAPS) approach.’ In D. Cervone and Y. Shoda (eds). The Coherence of Personality: Social Cognitive Bases of Consistency, Variability and Organisation, New York: Guildford, 37–67. Mulvihill, Senserrick and Haworth (2005). ‘Development of a model resource for parents as supervisory drivers.’ Monash University Accident Research Centre. OECD (2006). ‘Young drivers: the road to safety.’ The Transport Research Centre. Paris: OECD Publishers. Pelsmacker, P.D. and Janssens, W. (2007). ‘The effect of norms, attitudes and habits on speeding behaviour: scale development and model building and estimation.’ Accident Analysis and Prevention, 39, 6–15. Regan, M.A. and Mitsopoulos, E. (2001). ‘Understanding passenger influences on driver behaviour: implications for road safety and recommendations for counter-
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measure development.’ (Report No. 180) Clayton, Australia: Monash University Accident Research Centre. Shope, J.T., Ragunathan, T.E. and Patil, S.M. (2003). ‘Examining trajectories of adolescent risk factors as predictors of subsequent high-risk driving behaviour.’ Journal of Adolescent Health 32, 214–24. Shope, J.T., Waller, P.F., Ragunathan, T.E. and Patil, S.M. (2001). ‘Adolescent antecedents of high-risk driving behaviour into young adulthood: substance use and parental influences.’ Accident Analysis and Prevention, 33, 649–58. Simons-Morton, B. (2007). ‘Parent involvement in novice teen driving: rationale, evidence of effects and potential for enhancing graduated driver licensing effectiveness.’ Journal of Safety Research (forthcoming). Simons-Morton, B.G. and Hartos, J.L. (2003). ‘How well do parents manage young driver crash risks?’ Journal of Safety Research, 34, 91–7. Tabman-Ben-Ari, O. (2006). ‘Couple similarity for driving style.’ Transportation Research Part F 9, 185–93. Victoir, A., Eertmans, A., Van den Bergh, O. and Van den Brouke, S. (2005). ‘Learning to drive safely: social-cognitive responses are predictive of performance rated by novice drivers and their instructors.’ Transportation Research Part F, 8, 59–74. Waylen, A. and McKenna, F. (2002). Pre-Drivers’ Attitudes Towards Driving. Paper presentation, 67th Road Safety Congress; ROSPA.
Chapter 2
Piloting a Telemetric Data Tracking System to Assess Post-training Real Driving Performance of Young Novice Drivers Robert B. Isler, Nicola J. Starkey, Peter Sheppard1 and Chris Yu2 University of Waikato, New Zealand 1 AA Driver Education Foundation 2 SmarTrak, New Zealand Introduction Evaluating the effects of driver training interventions is a difficult research affair. The ultimate goal of such interventions is to make the driver safer and therefore less likely to be involved in a road crash. A particular driver training intervention can only be considered to be effective if it can show a significant reduction in the number of crashes for the driver or a significant change in driver behaviour that clearly implies safer driving. Getting accurate and comprehensive crash records is difficult and to measure post training behavioural driving changes based on self-reports (for example, log books) may not be accurate enough to be statistically meaningful. The majority of driver training evaluation studies in the last 30 years concluded that driver education and training contributes little to reducing crash risk or involvement for road users (pre-licence, defensive, advanced or driver improvement). And even more puzzling and paradoxical is the fact that there was no evidence that professional driver training is effective in reducing crash risk. However, failing to find a driver training effect does not necessarily mean that it does not exist. In fact, there has been a heated scientific debate about the usefulness of the hypothesis testing procedures employed by most of these evaluation studies (Shrout, 1997). For example, the fact that statistical procedures are generally geared towards preventing type one errors (claiming an effect when there is in fact no effect) but at the same time are quite likely to lead to type two errors (failing to detect an effect when there is an effect) biases results towards non significance. Furthermore, Crick and McKenna (1991) maintained that the lack of evidence for the benefits of road safety education and training may be ascribed to a lack of methodological soundness in previous evaluations and/or to the content of the course. It is indeed interesting to note that many driver training evaluations have been published as technical reports and therefore were not subject to peer review. Often, evaluation studies have failed to use appropriate control groups and have used
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hypothesis testing procedures inappropriately, with very little statistical power to detect any effects. The content of the driver training courses that have been evaluated in the past tended to emphasise the teaching of vehicle control skills or alternatively, were classroom based. Since then, research has shown that increasing driver skills does not necessarily lead to more capable drivers. For example, skid training may lead to drivers over-estimating their own driving ability, without actually improving the way they manoeuvre the car (Gregersen, 1996). Furthermore, studies suggest that crash involvement is more often the result of risk taking behaviour, rather than poor driving ability (Clarke, Ward and Truman, 2005). Thus, driver training programmes which concentrate on vehicle handling skills may actually lead to increased risk taking due to learners’ inflated self confidence and self-rated skills. Consequently, a growing consensus amongst driver training and road safety researchers is that greater emphasis should be placed on higher level cognitive functions underlying driving skills (Senserrick, 2007). Some researchers have argued further that there is an urgent need for a holistic and structured plan of education and training that addresses all goals of driver education, as outlined in the ‘Goals for Driver Education’ (GDE) model (see Engstroem, Gregerson, Hernetkostki, Keeskinen and Nyberg, 2003 for a comprehensive review on young drivers, driver education and training). At the same time there is a call for employing more sensitive and objective behavioural outcome measures, so that their accuracy can be increased and at the same time the probability for committing a type two error can be minimised. We recently conducted a large scale driver training study (Isler, Starkey, Charlton and Sheppard, 2007) in New Zealand to compare the effects of training in higher level driving skills (such as eye scanning, hazard detection and risk management) and vehicle control skills (such as manoeuvring, braking and parking) on teenagers’ real driving and risk taking behaviour, confidence levels and self-rated driving skills. Thirty-six teenage drivers (across a range of ethnic and social backgrounds) on a restricted driver licence were recruited via 500 secondary schools. After the driver training camp, we installed telemetric data trackers in the vehicles of eight participants to pilot how well this technology measured post-training real driving behaviour. We tracked the driving behaviour of the participants for 32 weeks in order to evaluate if such data acquisition could help fill a methodological gap in driver training evaluations. From the outset, we knew that the number of data trackers would be too small to make conclusive claims about any potential long-term effects of the driver training in our study. The idea was to test this new and promising evaluation technology and report on our findings. Method Participants From a total of 36 participants who took part in the driver training study, eight participants (four males and four females) who brought their private vehicles to the training session were selected to participate in this pilot study. They were all 16 years
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old and were required to hold a current New Zealand restricted driver’s licence. This ensured that they all had some unsupervised driving experience. Their vehicles were fitted with a telemetric data tracking system and their driving behaviour was monitored on-line via the internet over a 32 week period. The telemetric data tracking system The tracking system consisted of a small credit card sized global positioning module (SmarTrak Lite GPRS/GPS) fitted with an accelerometer (see Figure 2.1). The system was powered by the vehicles’ battery (six volt). It took approximately 30 minutes to install the system in a vehicle. In order to obtain accurate data, the device had to be pointing forward and on a flat surface. In most cases it was installed below the driver’s seat.
Figure 2.1 The telemetric data tracking system This system uses a GPS receiver and provides reliable and accurate navigational data. The software for the tracking and reporting interface via the internet was developed by SmarTrak Ltd (www.smartrak.co.nz). It allowed us to monitor, in real time, the driving performance (updated every two seconds) of the eight participants on the computer screen (see Figure 2.4 as an example of a map-based online tracking). The built-in accelerometer also provided g-force data from the vehicles. Daily, weekly and monthly reports of the driving measures for each participant could be produced and downloaded as a Windows Office Excel spreadsheet. The following driving measures were used as dependent variables in this study: Distance driven The number of kilometres driven for each trip was recorded. Number of trips A trip started from a ‘key on’ event (starting the engine of the vehicle) to a ‘key off’ event (shutting down the engine). Mean speed per trip Every four kilometres the current speed was recorded and the mean speed for each trip was calculated. Maximum speed
The maximum speed was recorded for each trip.
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Driver Behaviour and Training – Volume III
Speeding violation The system noted each time a participant exceeded 100 km/h (62 mph), which is the maximum speed limit for New Zealand. Lower speed limit violations (for example, driving 60 km/h on a road with a 50 km/h speed limit) were not monitored. Large g-force Each time the vehicle created a g-force (longitudinal or lateral) that was larger than 0.50, an event was triggered. The threshold setting was the same as the one used by McGehee, Raby, Carney, Lee and Reyes (2007) for their event-triggered video driver intervention trial. Negative longitudinal g-force events indicated hard braking while positive events indicated levels of acceleration that would be difficult to reach without external impacts (for example, rear end collision). The system did not allow differentiation between longitudinal g-forces created by hard braking and those created by hard cornering or swerving. Results Thirty-six participants (15 females, 21 males) attended the driver training study where they were first asked to complete a number of psychometric instruments and driver behaviour questionnaires. The data from these pre-assessments are currently being analysed. Participants were asked to rate how safe they felt driving in a variety of situations on a five point Likert scale (1 = Very Safe to 5 = Very Unsafe; adapted from Bergdahl, 2005). The responses from the eight participants in this pilot study did not differ significantly from the responses of the other participants in the driver training camp, and therefore the results from all participants (n = 36) are presented in Figure 2.2. Most participants felt safe in the majority of driving situations, except after drinking (rated between unsafe and very unsafe), when they are sleepy or tired and when they are angry or being tailgated (rated as between ‘neither safe nor unsafe’ and ‘unsafe’). Interestingly, they felt quite safe speeding at 120 km/h even though they indicated in a different questionnaire that speeding is one of the most frequent causes of young driver crashes. We received valid telemetric driving behaviour data from six of the eight participants for the entire 32 weeks period. The data for one of the six participants (#8) was not analysed, as the tracking system did not provide the data for the variable ‘distance driven’. Two of the participants crashed during the study and the GPS system allowed us to examine their driving behaviour just before (and, in one case, during and after) the crash. Participant #1 crashed in week 19. The tracking system did not transmit any data during the crash as the power supply was disrupted, and we were not able to retrieve any data from the tracker in the crashed car (see Figure 2.3). The last data we received from the vehicle was two minutes before the crash occurred, indicating that the vehicle was travelling at 75 km/h sometime within that time period. The participant’s account of the crash was as follows:
Piloting a Telemetric Data Tracking System
Figure 2.2
21
Mean responses and 95% confidence intervals of the participants in the driver training study (N = 36) for the questions: How safe do you feel driving: 1) at night? 2) in an unfamiliar area? 3) in the city? 4) in bad weather? 5) after drinking? 6) when sleepy or tired? 7) towing a trailer? 8) an unfamiliar car? 9) when angry? 10) when being tailgated? 11) at 100 km/h? 12) at 110 km/h? 13) at 120 km/h?
Hit a stationary vehicle parked half on/half off road. Was travelling at about 100 km/h when hit the vehicle. I just did not see the car – obviously lack of concentration. I was not text messaging or using phone prior to crash. I did have a passenger, though I can’t remember all that happened so I don’t know what I was doing to not see the car.
The participant suffered only some minor injuries but was shaken by the experience and decided not to drive for a while. Participant #2 crashed in week 30. She started her journey at 6.24 am, lost control on a bend at 7.22 am and swerved 180 degrees when she was hit by an oncoming car. For this incident we have a complete set of telemetric data available as the car was still functioning after the crash and power was continuously supplied to the data tracker. Figure 2.4 shows the map function of the on-line monitoring system listing the transmitted driving events on the right side of the map. The map revealed that the crash happened at 7.22 am and was preceded by a large negative g-force (–0.56),
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Figure 2.3 The crashed car of participant #1 probably caused by hard braking. At that time, the vehicle was travelling at 83 km/h when it swerved 180 degrees and hit an oncoming car creating a very large positive g-force (2.85). Within the same minute (7.22 am) the car was decelerated to 1 km/h. We later received the information that the crash occurred during very wet driving conditions. The vehicle of participant #3 was stolen in week five, in an early morning at 2.43 am. It seems that the vehicle was used for a ‘joy ride’ that lasted 11 minutes. Telemetric data showed that the car created seven negative large g-forces (up to –0.65), possibly indicating unsafe driving before the data flow was interrupted at 2.54 am. We were later informed that the car was found burnt out in a remote parking area. Table 2.1 shows the mean weekly distance driven (in kilometres, 1 km = 0.62 miles), number of trips and mean speed per trip for seven participants. As previously mentioned, the data from participant #8 could not be analysed. The table reveals that participants #1 and #2 travelled much longer weekly distances, compared to the other participants. Participant #7 had the smallest mean weekly number of trips. In addition, it is apparent that the weekly mean speeds per trip were by far the highest for participants #1 and #2. Table 2.2 summarises the mean weekly maximum speed, number of speeding violations per 100 km and number of large g-forces per 100 km for seven of the eight participants. It shows that participants #1 and #2 had the highest mean weekly maximum speeds. The number of mean weekly speeding violations per 100 km was highest for participant #4, followed by participant #1. All participants had a great number of mean weekly large g-forces, with participants #1 and #2 having the two largest numbers. Participants #2, #5, #6 and #7 had some weeks without driving.
Piloting a Telemetric Data Tracking System
23
Figure 2.4 The map function of the on-line monitoring system Figure 2.5 shows mean weekly maximum speeds for participants #1 and #2 who crashed during the 32 week period after the driver training study. As Figure 2.5 shows, these participants had lower mean maximum speeds right after the driver training study with participant #2 keeping to the New Zealand maximum speed limit of 100 km/h for the first six weeks before there was a substantial increase in her maximum speed in week seven, and more or less maintaining it until she crashed in week 19. Participant #1 had much higher mean weekly maximum driving speeds, which in some weeks reached up to 140 km/h. She had maximum speeds reaching 120 km/h for most weeks, except for the first two weeks after the driver training study and the two weeks following her crash. Figure 2.6 shows the mean weekly maximum speeds for participants #3–#7. The speeds varied considerably for all participants, except for participant #5 who reached maximum speeds at around 100 km/h for most of the monitored weeks. The other participants often reached maximum speeds of up to 120 km/h with participant #6 reaching speeds close to 140 km/h (week 32). Discussion Driving behaviour research literature has identified a need for more sensitive and objective intervention outcome measures. Thus, the aim of this pilot study was to test a telemetric data tracking system to measure post-training driving behaviour of young novice drivers. Specifically, this pilot study evaluated a tool that could help close a methodological gap that seems to exist in evaluation research of driver training interventions.
The mean (M) weekly distance driven (Dist) in kilometres (km), number of trips (Trips) and mean speed per trip (Mean Speed) in kilometres (km/h) for seven of the eight participants. Standard Deviations (SD), minimum (Min) and maximum (Max) values are also given
Part
Dist (km)
Trips Max
M
SD
Min
Max
M
SD
Min
Weeks
M
SD
Max
#1
512
290
23
1317
47
14
11
74
81
5.3
60
89
1–32
#2
460
307
0
991
31
25
0
69
84
2.5
0
90
1–18
#3
206
160
5
499
43
19
14
66
51
8.6
40
63
1–6
#4
199
111
59
340
25
12
4
54
69
19.6
18
93
1–32
#5
242
827
0
827
46
32
0
168
65
14.7
0
87
1–32
#6
339
217
0
962
74
45
0
139
54
22.4
0
94
1–32
#7
110
63
0
270
33
11
0
5
69
17.3
0
84
1–32
Driver Behaviour and Training – Volume III
Min
Mean Speed (km/h)
24
Table 2.1
Table 2.2
Weekly means of maximum speed in km/h (Max Speed), number of speeding violations per 100 km (Speeding Viol) and number of large g-forces per 100 km (G-force) for seven of the eight participants
Part
Max Speed(km) SD
Min
Max
M
SD
Min
G-force (km/h)
Max
M
SD
Min
Weeks Max
#1
123
9.4
89
141
8.7
6.1
1.3
22.8
81
5.3
60
89
1–32
#2
112
9.4
97
124
1.9
3.0
0.0
10.7
84
2.5
0
90
1–18
#3
96
25.5
68
117
0.4
0.4
0.0
1.1
51
8.6
40
63
1–6
#4
98
27.5
27
126
8.9
10.1
0.0
31.4
69
19.6
18
93
1–32
#5
100
19.3
0
111
7.3
10.3
0.0
32.8
65
14.7
0
87
1–32
#6
111
32.9
0
138
3.8
5.4
0.0
23.5
54
22.4
0
94
1–32
#7
86
620
0
121
2.2
5.2
0.0
21.7
69
17.3
0
84
1–32
Piloting a Telemetric Data Tracking System
M
Speeding Violations
25
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Driver Behaviour and Training – Volume III
Figure 2.5 Mean weekly maximum speeds for participants #1 and #2 Note: Participant #1 crashed in week 30 (C#1) but continued to drive in week 31 and 32. Participant #2 crashed in week 19 (C#2) and stopped driving.
Figure 2.6 Mean weekly maximum speeds for participants #3–#7 We received valid post-training real driver behaviour data from seven of the eight participants. Two participants, both living in rural areas, crashed their cars within the monitoring period, without being seriously injured. Their telemetric data indicated
Piloting a Telemetric Data Tracking System
27
that they were travelling longer distances, had higher average speeds and achieved higher maximum speeds than any of the other participants. It is interesting to note that road crash statistics in New Zealand indicate that young drivers in rural areas are at greater risk of being involved in a severe crash, than those who live in urban areas. Consistent with our data, these drivers normally have a higher risk exposure as they typically drive longer distances and more frequently use rural roads that allow for higher speeds than roads in urban areas. Speeding is known to be one of the most important factors of teenage crashes in New Zealand. However, our participants indicated that they felt relatively safe when speeding, even at speeds as high as 120 km/h. This is a particularly interesting finding, as most of the participants were aware that speeding is one of the most common causes of road crashes. Most participants in this study had maximum speeds reaching 120 km/h and some of them had speeds up to 140 km/h. It seems pertinent that driver training interventions should involve methods that could decrease this high risk behaviour. One of these methods could involve hazard anticipation training using video simulation, which clearly improved speed choice behaviour (McKenna, Horswill and Alexander, 2006). All participants had many large g-force events, either caused by hard braking (longitudinal g-force), and/or hard cornering/swerving (lateral g-force). Our tracking system was not able to differentiate between these events and perhaps recorded also some non risky g-forces caused by hitting a bump/pothole in the road. An event-triggered video recording system manufactured by DriveCam and used by McGehee, Raby, Carney, Lee and Reyes (2007), for their eventtriggered video driver intervention trial could help verify the cause of each large g-force. Hard braking events could have been caused by long hazard detection times of the participants, which are typically 30 per cent longer in inexperienced novice drivers compared to experienced drivers (Deery, 1999). Hazard detection times have been found to be related strongly to crash risk in young drivers and can be improved using road commentary methods or video based hazard detection training. In summary, the telemetric data tracking system used in this study seems to be a promising research tool for evaluating post-training effects by providing an objective and sensitive driver behaviour outcome measures. By using the map-based tracking function, all the recorded driver behaviour events, including crashes, could be mapped, replayed and analysed in detail on the internet. It also allowed us to create daily, weekly and monthly reports of important risk taking behaviour variables (such as speeding, average speed and large g-forces) and could also provide information on risk exposure (driving distance). In order to improve the system, an event-triggered video recording system could help verify each large g-force that was created by the monitored vehicles. It would also be beneficial to record lower speeding events such as driving 60 km/h on a road with a 50 km/h speed limit, but this depends on GPS based speed limit data for all roadways being available. To fully evaluate the utility of this system and the effects of a driver training intervention, ideally the tracking device would be installed into the vehicles of the
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participants several months before the driver training programme, in order to obtain data based on the participants real driving behaviour. Baseline driving behaviour in experimental and control participants can then be established, so that any potential changes in the post–training driving behaviour of the experimental group can be clearly attributed to the effect of the driver training. Acknowledgments We would like to thank the Accident Compensation Corporation, the Road Safety Trust and the ITO for funding this project. Thanks to the board of the AA Driver Education Foundation and to the a2om Driving Academy (UK) who both inspired us to conduct this study. We are grateful to all people who helped us to make the ‘frontal lobe’ training camp possible. And finally, a very special thanks to all 36 participants who invested two weeks of their holidays to become safer drivers. References Bergdahl, J. (2005). ‘Sex differences in attitudes towards driving: a survey.’ The Social Sciences Journal, 42, 595–601. Clarke, D.D., Ward, P. and Truman, W. (2005). ‘Voluntary risk taking and skill deficits in young driver accidents in the UK.’ Accident Analysis and Prevention, 37, 523–9. Crick, J.,and McKenna, F.P. (1991). ‘Hazard perception: can it be trained?’ In G.B. Grayson (ed.). Behavioural Research in Road Safety II. Proceedings of a seminar at Manchester University 17–18 September 1991. Crowthorne, Berkshire: Transport Research Laboratory,100–107. Deery, H.A. (1999). Hazard and risk perception among young novice drivers. Journal of Safety Research, 30 (4), 225–36. Engstrom, I., Gregersen, N.P., Hernetkostki, K., Keeskinen, E. and Nyberg, A. (2003). Young Novice Drivers, Driver Education and Training. Swedish National Road and Transport Research Institute, VTI rapport 491A 2003. Gregersen, N.P. (1996). ‘Young drivers’ overestimation of their own skill – an experiment on the relation between training strategy and skill.’ Accident Analysis and Prevention, 28 (2), 243–50. Isler, R.B., Starkey, N.J., Charlton, S. and Sheppard, P. (2007). ‘The “frontal lobe” project: a double blind, longitudinal study of the effectiveness of higher level driving skills training to enhance executive functioning in young drivers.’ Interim report to the ACC and Road Safety Trust, New Zealand. McGehee, D.V., Raby, M., Carney, C., Lee, J.D. and Reyes, M.L. (2007). ‘Extending parental mentoring using an event-triggered video intervention in rural teen drivers.’ Journal of Safety Research, 38, 215–27. McKenna, F.P., Horswill, M.S. and Alexander, J.L. (2006). ‘Does anticipation training affect drivers’ risk taking?’ Journal of Exp Psychol Appl., 12 (1): 1–10. Senserrick, T.M. (2006). ‘Reducing young driver road trauma: guidance and
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optimism for the future.’ Injury Prevention, 12 (1): 56–60. Shrout, P.E. (1997). ‘Should significance tests be banned?’ Psychological Science, 8 (1): 1–2.
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Chapter 3
Fault Correction or Self-Assessment: Which Way Forward? Ian Edwards and Tracey Curle Alpha to Omega Motoring Ltd. (a2om), UK Introduction Many studies (for an overview see Engström et al., 2003) have identified that young novice drivers are over-represented in road crashes. Extensive research over many years has been carried out in order to identify the factors that may contribute to this phenomenon. Education has often been identified as a key counter-measure but there has been little evidence (Mayhew and Simpson, 2002) to suggest that driver education has been successful, although some studies (McKenna et al., 2006) have identified that computer-based, hazard perception training can be beneficial. Recently, Hattaka et al. (1999) developed the Goals for Driver Education (GDE), a four level hierarchical approach to driver education. The top level of the matrix looks at the driver’s ‘Goals for life and skills for living’, the next level looks at ‘Goals and context of driving’, with the two lower levels covering ‘Mastery of the traffic situation’ and ‘Vehicle manoeuvring’. Based on the approach outlined in the GDE, this paper will argue that the current system of developing and training driving instructors in the UK is too limited and that a wider approach is required. Assessment and training of drivers in the UK The UK’s driving test is conducted by the Driving Standards Agency (DSA) and is a fault-based assessment of a driver’s ability at the time of the test. The test asks a driver to drive under test conditions with the examiner assessing the drive for any faults committed. These faults, if serious enough, will be recorded and are subdivided into driving faults, serious faults and dangerous faults. The test candidate can make up to 15 driving faults and pass their driving test, but any serious or dangerous faults committed result in failure. This system has served the UK well since its introduction in the 1930s and has made a significant contribution to the UK’s road safety record. However, it appears likely that the driving test informs the content of the training which is delivered by driving instructors. This fault-based system of assessment has also significantly impacted on the way instructors are assessed by the DSA, who are also responsible for monitoring the standard of instruction. The quality assurance process for driving instructors consists of a periodic observation of a driving lesson by a specially trained DSA examiner. The examiner will mark the instructor’s performance on a number of items but most importantly three ‘core competencies’, which are:
Driver Behaviour and Training – Volume III
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• • •
fault identification – ability to identify a fault committed by the driver fault analysis – ability to analyse why and how the fault was committed fault correction – ability to offer strategies to correct the fault
These core competencies are marked on a one to six scale with six indicating a high level of competency. At the end of the assessment procedure the instructor will be awarded an overall grade, again on a one to six scale with six being the highest. This overall grade is strongly associated with the grades achieved on the three core competencies. The authors of this paper, based on the developmental work undertaken by the a²om academy, argue that this fault-based assessment system is too narrow and leads to restrictive and negative learning outcomes. The in-car learning environment It is widely established (for an overview see the OECD report, 2006) that the goals, context and motivation of a journey are important factors in determining how the driving task is completed. It is therefore likely that the motivation of a learner driver will have a major bearing on the type of faults committed. Edwards (2005) suggested that the true personality of a driver is rarely seen by the driving instructor during driving lessons, as the driver will be motivated to take on the social role advocated by their instructor. This is in line with Parker and Stradling (2001) who suggested that once the driving test has been passed the driver will enter an ‘Expressive Phase’. In this phase the driver will drive in a way which expresses their own personality and beliefs about driving. If this is correct, it follows that the faults identified by the instructor whilst training a driver may be limited to technical driving errors. It is therefore very unlikely that an instructor, using a fault-correction based approach to driver education, will actively engage in discussions about the driver’s own personality traits and beliefs. This argument also strengthens the case for the use of psychometric profiling for new drivers. Psychometric profiling would help the instructor to have an understanding of the learner driver’s own beliefs, thus allowing the instructor to tailor the tuition to fit the individual’s own profile. This argument could also be extended to cover other aspects of driver training and evaluations have shown this to be an encouraging area of research (Dorn and Garwood, 2005). The types of faults seen by driving instructors may also be limited by the context in which the driving task takes place. On a driving lesson fewer external issues are present, compared to a ‘real world’ journey. The learning environment in many ways bears little similarity to those in which the real driving task will take place. Examples of this include: there are fewer time pressures on the driver during a driving lesson, the learner driver’s goal is to drive well and not to complete a ‘real’ journey, generally there is no one else other than the instructor present and so on. In this context the faults committed are likely to be those only attributed to lack of knowledge, psychomotor skills and inexperience, rather than those associated with outside factors and influences such as peer pressure, stress, fatigue and so on. As
Fault Correction or Self-Assessment
33
these pressures are not generally present in the learning environment it is unlikely that the instructor will ever see how the driver is likely to cope with these issues later in their driving career. In order to provide a student with a complete driver education, the GDE outlines that the student must be made aware of the types of issues they will face when driving in the ‘real world’ and not simply how to avoid common errors committed whilst training. A study looking at the tuition received by 30 learner drivers delivered by four top grade instructors seemed to indicate that this holistic approach was not being delivered: The nature of the comments made by instructors indicated that instruction was carefully tailored to the situation at hand, and very little weight was placed on aspects of the driving task which the pupil did not have the opportunity to actually carry out. There is thus little scope for pupils to develop the ‘theoretical’ understanding of the driving task which may serve them well when confronting novel circumstances later in their driving career. (Groeger and Clegg, 2000).
Groeger and Clegg’s (2000) study also identified that, as the students improved and committed fewer errors, the amount of input from the tutors decreased. Whilst this approach is likely to develop the ability of the driver to control the car and integrate in traffic, it is unlikely that it will address the context in which driving takes place once the driver acquires a full driving licence. In the early stages of learning to drive, a great deal of detail can be lost by the learner driver as they struggle to gain the psychomotor skills required to drive. As skill levels increase, the learner is more able to engage and absorb information on other topics related to driving. It is therefore hypothesised by the authors that the most effective time to engage in dialogue related to the higher elements of the GDE would be at the later stages of learning, once car skills have been mastered. Figure 3.1 shows the traditional approach to driver training as outlined by Miller and Stacey (2006) in the Driving Instructors’ Handbook, which is recommended reading by the Driving Standards Agency for all trainee driving instructors. The model indicates that when a driving instructor first introduces a new topic the learner driver will require a high level of input. This input reduces over a period of time until the learner driver has reached a level of competency whereby they are able to undertake the task independently. Miller and Stacey refer to this as the learning curve and outline that You will be able to see the manipulative aspects of the driving task improving by simply watching your pupil execute them. However, prompts and verbal guidance during the early stages of learning will still be necessary. When the skill is sufficiently developed, hints and reassurance should be all that is required (Miller and Stacey, 2006: p. 261).
Whilst in many ways this approach seems to be intuitively correct, that is that as a driver improves there should be less need for the instructor to intervene, this approach fails to take into account the environment in which the faults are committed. As already identified, the faults committed whilst training are unlikely to be the faults committed in the ‘real world’. Therefore, instructors need to actively engage
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in discussions which will help the learner driver identify their own strengths and weaknesses against a ‘real world’ backdrop. This approach would require instructors to take a wider approach to driver education and would be much more in line with coaching methods. Whitmore (2006) defines coaching as ‘unlocking a person’s potential to maximise their own performance’ (8).
Prompted Practice
Transferred responsibility
Achievement
Controlled Practice
Time
Figure 3.1 Miller and Stacey’s driving instruction learning curve This coaching approach is critical in helping new drivers to understand and develop self-assessment skills, a key element contained within the GDE matrix. This approach needs to be used to address all four levels of the GDE so that, once a driver has passed the driving test, they are able to self-learn from the mistakes they commit in the ‘real world’ environment. Boud (1995) identified that ‘for effective learning of any kind to take place, learners – whoever they may be – must develop the capability of monitoring what they do and modifying their learning strategies appropriately’ (14). Boud also suggests that graduates who develop self-assessment skills will be more likely to: • • • •
wish to continue their learning know how to do so monitor their own performance expect to take full responsibility for their actions and judgements.
If the above were applied to a driving context, it could have significant results by accelerating the ability of a newly qualified driver to learn more quickly from the experience they gain in the first few months of post-test driving. This concept of
Fault Correction or Self-Assessment
35
learning from their own experience is critical as it is almost impossible for a learner driver to experience all of the possible driving situations they may face in the future within the pre-test learning environment. Therefore, the role of self-assessment is crucial and instructors should actively encourage the development of this attribute both in themselves and the drivers they train. Many instructors would argue that this is already being done and there could be some truth in this as instructors are assessed on their ability to use an appropriate question and answer technique. However, as this assessment is generally linked to the identification of faults, the issues being discussed are very likely to be those associated with the two lower levels of the GDE, namely vehicle manoeuvring and mastery of traffic situations, rather than the higher GDE elements. The role of the instructor One of the key ways in which self-assessment could be used by the in-car instructor is for the instructor to encourage the learner driver to think about situations they may be faced with in the future. In this way, the learner driver is given the opportunity to consider their own predisposition towards certain risk-increasing activities and to develop and rehearse possible strategies. This rehearsal could help the new driver by providing bench marks to assess performance, as well as practising the selfassessment techniques in the development of those bench marks. Conclusion The current fault-based assessment system places too much emphasis on the ability of the instructor to identify and deal with student errors, with too little emphasis on encouraging discussion of higher level knowledge and skills as outlined in the GDE. It is important to remember that fault correction does have an important role to play in the development of a driver, but it is equally important to recognise the limitations of this approach. It is doubtful that any in-car training initiative aimed at reducing the crash rate of young drivers will be successful whilst training continues to be predominantly aimed at the identification, analysis and correction of faults, to the detriment of the development of appropriate self-assessment skills. The a²om academy is pioneering this approach by placing the development of self-assessment skills at the centre of their driver education programmes. References Boud, A. (1995). (ed. by Boud, D.). Enhancing Learning through Self-Assessment. Routledge Falmer, West Sussex, UK: 15 Dorn, L. and Garwood, L. (2005). ‘Development of a psychometric measure of bus driver behaviour’. Behavioural Research in Road Safety: 14th Seminar.
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Department for Transport, London. Edwards, I. (2005). Behavioural Research in Road Safety 2005: Fifteenth Seminar. DfT publications: West Yorkshire, UK. Engström, I., Gregersen, N.P., Hernetkoski, K., Keskinen, E. and Nyberg, A. (2003). Young and Novice Drivers, Driver Education and Training – Literature Review. Swedish National Road and Transport Research Institute: Sweden. Groeger, J.A., and Clegg, B.A. (2000). Practice and Instruction When Learning to Drive. Road Research Report No 14. London: HMSO. Hattaka, M., Keskinen, E., Gregersen, N., Glad, A. and Hernetkoski, K. (1999). Results of EU-Project GADGET, WP3. Parker, D. and Stradling, S. (2001). Road Safety Research Report No 17 Influencing Driver Attitudes and Behaviour. DfT publications: West Yorkshire, UK. Mayhew, D.R. and Simpson, H.M. (2002). ‘The safety value of driver education and training.’ Injury Prevention, 8: ii3–ii8. McKenna, F.P., Horswill, M.S. and Alexander, J.L. (2006). ‘Does anticipation training affect drivers’ risk taking?’ Journal of Exp Psychol Appl., 12 (1): 1–10. Miller, J. and Stacey, S. (2006). The Driving Instructor’s Handbook. Kogan Press: London: 261. OECD. (2006). Young Driver – The Road to Safety. Paris, France.
Chapter 4
New Elements in the Dutch Practical Driving Test: A Pilot Study 1
Jan Vissers,1 Jolieke Mesken,1 Erik Roelofs2 and René Claesen3 DHV Environment and Transportation, Amersfoort, The Netherlands 2 Cito National Assessment Institute, Arnhem, The Netherlands 3 CBR Driving Test Organisation, The Netherlands
Introduction In line with many other countries, novice drivers in the Netherlands are greatly overrepresented in traffic collisions and fatalities, and male novice drivers even more so. While in the Netherlands the relative risk of being involved in a road accident has been decreasing over the last few years (Stipdonk, Aarts, Schoon and Wesemann, 2006), for young drivers aged between 18 and 24 this is not the case. These developments have led the Dutch Traffic Department to develop a plan to revise the Dutch practical driving test. It commissioned the Driving Test Organisation (CBR) to develop and implement a new practical driving test by January 2008. The new practical driving test should, in line with the Goals for Driver Education research (GDE-matrix; see Siegrist et al, 1999) include new elements that enable the assessment of higher order skills, such as hazard perception and self-reflection. In 2007, pilot studies have been carried out by DHV in close cooperation with the CBR, to investigate the applicability of these new elements of the practical driving test. In this paper the results of these pilot studies (Vissers and Mesken, 2007) are described. Background of high accident risk of novice drivers Knowledge about what constitutes a safe and unsafe driver is quite extensive. A vast amount of research is available, which shows that becoming a safe driver is a very complicated matter. In two recent literature reviews (OECD, 2006; Engström, Gregersen, Hernetkoski, Keskinen and Nyberg, 2003) a lot of important correlates with unsafe driving have been documented. Compared with experienced drivers, young novice drivers have more single vehicle crashes, more often lose control of the car and more often drive too fast considering the circumstances just before the crash. They have relatively more weekend crashes and their passengers are more often of their own age group. The causes of the high accident rate of young novice drivers can be clustered around two main accident causes: first, age-related factors and second, inadequate skills.
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Age-related factors The age-related factors (or ‘age cluster’) refer to the developmental phase of the young people at that particular moment. The moral, emotional and cognitive development of young women is slightly different from that of young men. Adolescents, and especially males, often rebel against existing norms, do not want to appear ‘soft’ to their friends, enjoy sensation, underestimate risks and have the feeling that they are more or less invulnerable. Here we are addressing things that are typical for youngsters, such as their lifestyle, peer group membership, the youth socialisation process and so on. These all influence attitudes, motives and the decisions which drivers make whilst driving. Although there are substantial differences between novice drivers in the extent to which these age related factors influence their driving behaviour, it is not the case that only a small core group of wild young men are completely responsible for the high accident rate. Young inexperienced female drivers also have distinctly higher crash rates than young experienced female drivers. There is often a sliding scale: the more one possesses certain personality features (for example, a greater need for excitement) and the more one belongs to a certain, more problematic subgroup, the higher the crash rate. Lack of adequate skills The lack of adequate skills is not so much a matter of poor vehicle control, but is more a lack of: • • •
being able to observe properly (observe relevant information); adequately judging traffic situations (that is, capable of concentrating on aspects of the traffic environment that are potentially dangerous); and predicting accurately how a particular traffic situation will develop.
These skills are generally referred to as ‘higher order’ skills. Furthermore, young novice drivers often have a lack of meta-cognitive skills. This means that young novice drivers’ self-assessment skills are inadequate, leading to not being able to fit their inadequate task skills (a lack of higher order skills) to the driving tasks they encounter in traffic. This process of fitting the task skill to the driving task is sometimes called calibration (Kuiken and Twisk, 2001). For instance, young novice drivers too rarely think ‘it is dark and raining, and as I don’t have enough experience yet, I will go by train this evening and not by car’. Calibration is not only important at this more strategic level, but also at the tactical level when driving in traffic. An example of this is ‘shall I overtake this car on this slippery road on this dark evening, given my lack of experience?’ A lack of higher order skills is mainly the result of a lack of driving experience. So this cluster of causes is also referred to as the ‘experience cluster’.
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Weight of both clusters of causes Several studies investigated the weight of the ‘age’ and the ‘experience’ clusters. Most of them (among others, Maycock, Lockwood and Lester, 1991; Gregersen and Bjurulf, 1996; Sagberg, 1998) have shown that a lack of driving experience is somewhat more important than the influence of age related factors. Data from the Periodic Road Safety Surveys in the Netherlands (Barten, van Drunen, Herber, IJsselstijn and Vissers, 2005) also show that about 60 per cent of accidents can be attributed to experience related factors and about 40 per cent to age related factors. So, in general, the high accident rate of young novice drivers is more determined by a lack of driving experience than by age related factors, although there is some variation between studies in just how dominant the experience effect is. Theoretical framework: GDE-matrix The information about the factors that are related to the high accident rate of young novice drivers has been structured into a hierarchical model. A useful description of the different levels of driving behaviour was developed as part of the EU-project GADGET (Siegrist et al., 1999): the matrix of ‘Goals of Driver Education’ or GDEmatrix. The matrix is based on the assumption that the driving task can be described as a hierarchy. The idea of the hierarchical approach is that abilities and preconditions on a higher level influence the demands, decisions and behaviour on a lower level. The GDE-matrix describes four levels, starting at the basic level with ‘Vehicle manoeuvring’ (level I) and then ‘Mastery of traffic situations’ (level II). The higher levels relate to ‘Goals and context of driving’ (level III; trip related) and ‘Goals for life and skills for living’ (level IV). These higher levels are less related to knowledge and skills and more to understanding, experience and self-awareness. Researchers participating in the GADGET project also emphasised the importance of providing feedback to drivers in order to address the higher level motivational and attitudinal components of driver behaviour. A key element of the GDE-matrix is self-evaluation – ‘how did I perform in this situation and what could I have done differently to reduce the risk?’ Many learner drivers in EU-countries are not trained well in these aspects of driving. Most countries are still focusing on traffic rules and managing/mastering the vehicle in different traffic situations, which is the historical basis for driver education and driver testing all over the world. The implication of the GDE-matrix is that focusing training and testing on the lower levels does not guarantee that drivers will drive safely once they start to drive unsupervised and are subject to level III and IV situations and goals. Indeed, focusing training and testing solely at levels I and II can have a negative effect on safety, increasing confidence and encouraging drivers to explore a wider ‘performance envelope’ without making them aware of the risks that this type of behaviour imply (Vissers, 2004a). Recent work carried out by the Transport Research Laboratory (Baughan et al., 2006) illustrates the possible negative effect of concentrating on the lower level of the GDE-matrix. This study highlighted that those drivers who have the fewest
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lessons prior to taking their test often have the fewest faults during their driving test and have the poorest attitudes towards safe driving – and subsequently go on to have more accidents than other novice drivers. This raises the question as to whether the content of the test is correct, that is, does the driving test assess the right things in the right way? New elements for the practical driving test Based on the findings of the GDE-matrix studies, four categories of new elements for the practical driving test have been distinguished: • • • •
independent driving ‘productive’ special manoeuvres perception and management of hazards self reflection
Independent driving Once novice drivers have acquired their licences, they will have to drive independently. Modernisation of the driving test means a lot of emphasis on the higher levels of the GDE-matrix which deal with driver characteristics and risk awareness. Independent driving appears to be a relatively easy way to introduce elements from the higher levels into the driving test, especially level III. As part of independent driving, trip-related planning and decision skills related to both safety and environmentally friendly driving can be integrated in the practical driving test. Requiring candidates to demonstrate responsibility and independent decisionmaking in the test, goes beyond merely learning technical vehicle control and applying traffic rules. In countries where driver training is not regulated and where there is no obligatory second phase, introducing this type of driving into the practical driving test means it will be incorporated into the driver training. Independent driving should be considered as a general ability. It should not be defined as a separate driving task but as something related to and affecting all driving tasks. It should be seen as a tool to test other elements, to lead a candidate to situations where they have to make their own choices. Responsibility and independence should be clear and present in all parts of driver-training curriculum and consequently in the driving test. Likewise, all the different parts and elements of the driving test should enhance and encourage independent driving. Examiners will need additional training to be able to apply and identify independent driving in the practical driving test. The driving test requires cooperation between an examiner and a candidate. It is important to determine how much freedom should be given to each. There needs to be a balance. Candidates should be given enough freedom so that they feel responsible for their own driving but examiners need to maintain enough control to be able to test all they need to test. In a driving test, independent driving involves creating circumstances in which candidates shows their driving skills over a longer period
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without the help or instructions of the examiner. During this period, candidates have to find their way and make decisions in an independent way.
Working definition of ‘independent driving’ Independent driving means that candidates make a responsible choice based on their own abilities and the requirements of the task. In the driving test, independent driving involves creating circumstances in which the candidate has to demonstrate their driving skills over a longer period without the help or instructions of the examiner. During this period, candidates have to find their way and make decisions in an independent way.
The following forms of independent driving were tested in the pilot studies: •
•
• •
Driving towards coordination points: The candidate is asked to drive towards a certain coordination point (for example, a railway station, hospital or school). Using a navigation system: The examiner enters a destination in the navigation system and the candidate then has to follow the directions given by the system. Fixed task: The candidate is instructed to continuously take the second street on the left followed by the second street on the right. Being given a series of instructions: The examiner gives the candidate a series of instructions comparable to a situation in which someone asks for directions when they are in unfamiliar surroundings.
‘Productive’ special manoeuvres As an alternative to the reproductive performance of the special manoeuvres in the current driving test, a more independent (or productive) way of carrying out these special manoeuvres has been introduced in the Dutch driving test. The ‘productive’ special manoeuvres can be seen as a special form of independent driving in which candidates have to make their own decisions about when and how to perform which special manoeuvre. As alternative approaches to the existing special manoeuvres the following productive special manoeuvres were tested in the pilot studies: •
•
Turning manoeuvre: • independently turning the vehicle around (the candidate has to determine where and how the vehicle should be turned) Parking manoeuvre: • independently looking for a parking space in a parking lot and parking the car • independently looking for a parking space in a street in a built-up area and
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• • •
parking the car Stopping manoeuvre: stopping directly behind another vehicle (in such a way that the candidates can follow their route without having to reverse first) stop and go: stopping parallel to a driveway and subsequently following the route
Perception and management of hazards In the practical driving test the decisions of the examiner are traditionally based on what they see: the decisions the candidate makes and the way in which they put these decisions into action. But whether the candidate also performs the processes that precede decision-making and vehicle handling (for example, perception of relevant information, making the right predictions and evaluations) in a proper way is not observable to the examiner. There are two possible ways to include information about the choices a candidate makes: questioning by the examiner and commentary driving by the candidate. The scope of these methods in the practical driving test is probably limited because there are difficulties in using verbal reports as a basis for inferring whether the right choices have been made and whether the behaviour is controlled by higher-level goals. In addition to this, formal pass/fail criteria may be difficult to establish. Nevertheless it can be helpful to integrate these methods to assess adequate maximum performance of the candidate, even though the information will not be used as formal pass/ fail criteria. The following two approaches for assessing hazard perception and hazard management were investigated in the pilot studies: • •
commentary driving by the candidate questioning by the examiner
Self-reflection by the candidate In Sweden and Finland self-reflection by the candidate is already added to the driving test. Candidates have to fill out a form and give an assessment of their own driving behaviour using the topics that are assessed at the driving test by the examiner. Examiners only use this self-reflection form after the test, when they give their final assessment. The candidate’s self-reflection is used to give better feedback to the candidate about their strong and weak points. This is useful information in the case of failure (what should be focused on in driver training in order to pass the test next time) as well as success (even though the candidate passed what weaknesses they should focus on during independent driving). The examiner more or less makes an assessment of their future driving safety. This is consistent with the claim that passing the test is not an ultimate goal but just a starting point for further learning to
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become an independent, safe and responsible driver. This approach to self-reflection was not tested during the pilot studies.1 Instead of self-reflection after the driving test in the pilot studies self-reflection during the driving test was tested. For self-reflection during the test the candidate is asked to stop the car after having driven for about 15 minutes. Then the examiner asks the candidate to reflect on their driving performance: ‘What went well and what could you have done better?’ The examiner reacts in a neutral way and a discussion based around the candidate’s faults is presented. In the pilot studies carried out, it was decided whether higher order skills can be assessed using the new elements in the new practical driving test. In addition the practical application of the new elements was evaluated. In summary, this leads to the following research questions: 1. To what extent are the newly introduced elements in the practical driving test considered applicable? 2. To what extent do, according to driving instructors and examiners, the newly introduced elements enable the assessment of higher order skills? Method Participants About 150 candidates, who were in the final stage of their driver training, practised with the new elements for about six weeks. After that, a Learner Interim Test2 was administered by the CBR in which the new elements were included. Instructor, candidate and examiner completed the evaluation forms. Due to incomplete forms, a total number of 109 sets were used for the analysis. The different regions in the Netherlands were represented equally in the sample. With the exception of a slight over-representation of older candidates in the study, the sample of candidates was representative for the population of CBR-candidates for the driving test. About twothirds of the candidates (65 per cent) were 18 or 19 years old, 15 per cent were between 20 and 25 years of age and the remaining 20 per cent were over 25 years of age. Background variables such as gender (49 per cent male), educational level and being employed did not differ between sample and population. Also the number of
1 The decision was already made to test self-reflection after the driving test (the Finnish/ Swedish model) as part of a follow-up experiment in which the most successful new elements will be integrated in a modernised driving test. 2 In the Dutch system, all learner drivers have the opportunity to take the TTT (Learner Interim Test or ‘Tussentijdse Toets’). This test usually takes place when candidates are about three quarters of their way through their driver training and gives the learner drivers a chance to show a CBR examiner how they drive. The test also gives learner drivers the chance to experience test circumstances and they can earn an exemption for the special manoeuvres if these are performed well. Following the test, the examiner will give the candidate feedback regarding what areas still need to be addressed.
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driving lessons (an average of 33 one-hour lessons until the moment of the Learner Interim Test) did not differ. Questionnaire Instructor, candidate and examiner completed a questionnaire immediately after the Learner Interim Test. The questionnaires were aimed to assess the effectiveness of the procedures for the new elements and included open-ended and closed questions concerning topics such as: the perceived difficulty of practising with the new elements, the perceived usefulness of the new elements for the practical driving test and the perceived possibility to assess higher order skills by means of the new elements. Furthermore, questions were asked about the time needed to prepare candidates for the new elements and the extent to which this was sufficient. Candidates also answered questions about background variables, in order to establish whether the sample was representative for the average candidate. Procedure and data collection Each candidate was tested on one of the four new methods of independent driving and one of the three ‘productive’ special manoeuvres. As well, each candidate was tested on either hazard perception or self-reflection during the test. So each candidate was tested on three new elements (out of the ten being introduced) during their Learner Interim Test. Learner drivers, driving instructors and examiners completed the questionnaire directly after the Learner Interim Test. Analysis In this pilot phase of the study the emphasis was on a qualitative analysis of the data. So a descriptive analysis and a comparison between the three groups involved (candidates, driving instructors and examiners) on the questionnaire measures was conducted. Results Independent driving Independent driving is not yet a self-evident part of driver training. According to learner drivers, independent driving takes some time to master. The easiest method to demonstrate independent driving in their opinion is driving to a coordination point (for example, a railway station, hospital or school): 49 per cent evaluated this method as (very) easy to learn and perform. They evaluated the use of a navigation system (41 per cent [very] easy) and being given a series of instructions (38 per cent) as somewhat more difficult. The method of continuously taking the second street on the left followed by the second street on the right was considered most to be the most difficult task (only 30 per cent evaluated this task as [very] easy). (See Table 4.1.)
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45
Perceived task difficulty of the four methods of independent driving (percentage responding that performance is ‘easy’ or ‘very easy’)
Driving to a coordination point Use of a navigation system Continuously taking 2nd left then 2nd right Given a series of instructions
Candidate 49%
Driving instructor 45%
Examiner 60%
41%
52%
38%
30%
24%
21%
38%
45%
40%
This is consistent with the opinion of the driving instructors. However, although the instructors agree with the learner driver that independent driving is difficult, they have more confidence that learner drivers can be trained to master independent driving. But to realise this, instructors believe that driver training will have to be reorganised. If independent driving is to be integrated as driver training right from the start, driving instructors expect it will only take a little extra time to learn this method. Driving instructors in our study seem very positive about the added value of independent driving for training as well as testing. According to them, independent driving gives more insight into the way the learner drivers detect important changes in traffic, how they evaluate these changes, how they plan their actions, select the most appropriate action under the actual circumstances and finally carry out the selected action. When we look at the assessment of the four methods of independent driving on the Interim Learner Test, the conclusions of the examiners are in line with the opinions of the candidates and driving instructors. According to examiners, candidates had some difficulty carrying out the four methods of independent driving in the desired way; especially the method of continuously taking the second street on the left followed by the second street on the right, which was considered difficult for candidates. Driving to a coordination point is the easiest way of independent driving: 76 per cent performed this method as required. Performance on the use of a navigation system and being given a series of instructions is somewhat more difficult: in both cases 55 per cent of the candidates performed as required. On the method of continuously taking the second street on the left followed by the second street on the right candidates showed the lowest performance: 37 per cent performed as required. With the exception of the method of continuously taking the second street on the left followed by the second street on the right, the tested methods seem applicable as an assessment instrument during the driving test. Independent driving is of great value for the driving test as well as for the training of novice drivers according to instructors and examiners. By assessing independent driving during the driving test, the candidates are forced to show a higher level of driving performance. The candidates have more opportunities to show ‘productive’ driving behaviour: they have to make their own choices in traffic and they are more
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aware of other traffic participants and the traffic environment. Examiners get a better insight into the way the candidate handles traffic situations and they can follow the candidate’s driving-related cognitive processes in response to changes in driving situations. ‘Productive’ special manoeuvres The ‘productive’ special manoeuvres are not a problem according to the learner drivers (see Table 4.2). The parking manoeuvre is somewhat easier to perform than the turning manoeuvre and the stopping manoeuvre. Learner drivers responded that they thought it is useful to have the ‘productive’ special manoeuvres integrated into the driving test instead of the traditional ‘reproductive’ special manoeuvres. In the future when they have their driving licence they will have to make independent choices concerning what special manoeuvre to perform and where to execute it. Table 4.2
Perceived task difficulty of the three categories of ‘productive’ special manoeuvres (percentage responding that performance is ‘easy’ or ‘very easy’) Candidate
Driving instructor
Examiner
Turning manoeuvre
57%
69%
39%
Parking manoeuvre
71%
61%
49%
Stopping manoeuvre
60%
70%
45%
Similarly, driving instructors stated that the performance of the ‘productive’ special manoeuvres creates no problems for learner drivers. In their opinion it will take little time to teach the new methods during driver training. Again, it will be more or less a matter of reorganising the training, so no extra time will be needed to train the learner drivers the new special manoeuvres. Driving instructors stated that by introducing the ‘productive’ special manoeuvres in training and testing, independent decision-making on a more strategic level is stimulated. Learner drivers have to improve their planning and anticipation behaviour. The examiners evaluated the ‘productive’ special manoeuvres somewhat differently to the learners and instructors, believing them to be more difficult, especially the turning and parking manoeuvres. According to the examiners, the ‘productive’ special manoeuvres are of great value to the driving test. As with independent driving, examiners expect that more insight is gained in the way the candidates detect important information about the traffic situation, evaluate this information, plan their actions, select the most appropriate special manoeuvre under the actual circumstances and finally carry out the selected special manoeuvre correctly.
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Hazard perception skills Concerning the two methods that have been introduced to measure candidate’s hazard perception skills, the commentary driving was judged across all groups to be very difficult for the candidates to perform. Although all groups agreed on the fact that commentary driving is a good method for driver training, they believed that it is not sufficient as an assessment instrument. Furthermore, all three groups believed that questioning on critical emerging hazards is a useful training method. But the method is evaluated somewhat more positive as an assessment instrument. According to examiners and instructors, questioning by the examiner gives insight into the way the candidate has solved a problematic traffic situation and makes clear whether they have acted deliberately or not. They add, however, that the verbal skills of candidates may be a factor for this particular method. In addition, this method is considered sensitive to subjective interpretations by the examiner. These disadvantages may threaten the reliability of the instrument. Self-reflection According to all groups the measurement of self-reflection during the driving test is not useful. In the first place it is questionable if the candidates are able to give a reliable picture of what their strong and weak points have been during the first ten or fifteen minutes of the driving test. Besides this, examiners felt that self-reflection feedback (especially when things didn’t go well during the first part of the test) can have negative consequences for performance on the remaining part of the test. Discussion The findings for the new elements introduced to measure independent driving on the driving test is that three out of the four methods tested in the pilot study are considered to be very useful: driving to a coordination point, use of a navigation system and being given a series of instructions. Independent driving is a good method to provoke more ‘productive’ driving behaviour: candidates have to take more responsibility and plan their own driving actions in advance. By introducing independent driving in the driving test, more attention is given to the third (strategic level) of the GDE-matrix. The results suggest that the examiner gets a better insight into the way the candidates detect important changes in traffic, how they evaluate these changes, how they plan their actions and select the most appropriate action under the actual circumstances and finally how they carry out the selected action. This conclusion also applies to the new elements introduced to assess whether candidates are able to make an independent choice about where to perform a special manoeuvre and about which special manoeuvre to perform. All three categories of ‘productive’ special manoeuvres that have been tested are considered very useful: the turning manoeuvre, the parking manoeuvre and the stopping manoeuvre. As is the case in independent driving, more insight is gained in the way the candidates detect important information about the traffic situation, evaluate this information,
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plan their actions, select the most appropriate special manoeuvre under the actual circumstances and finally carry out the selected special manoeuvre correctly. Two methods to assess hazard perception skills have been tested. Commentary driving is not considered a useful method for the driving test. The questioning of a critical traffic situation by the examiner is considered somewhat more favourably as an assessment instrument. Questioning by the examiner gives insight into the way the candidate has solved a problematic traffic situation but the reliability of this assessment instrument can be problematic. It may be useful to include questioning by the examiner in the driving test for countries with a ‘test-driven licensing system’ that relies on the test to induce drivers to take adequate training, as is the case in the Netherlands. Questioning by the examiner to assess hazard perception will mean that driving instructors will need to provide hazard perception training as well. Because of serious threats to the reliability of the instrument, formal pass/fail criteria may be difficult to establish. It was decided therefore not to include results from questioning into the final pass/failure decision. In this pilot study, a candidate’s self-reflection was measured during the driving test. Self-reflection integrated in the driving test is not a useful assessment method, according to the participants in this study. It is questionable whether reliable information is gained about the strengths and weaknesses of the candidate’s skills. Additionally, examiner’s feedback on their self-reflection can affect driving test performance for the rest of the test. On the basis of the results of the pilot study a new procedure for the Dutch driving test has been outlined. The main changes concern the introduction of: •
• • •
independent driving (a substantial part of the test will consist of independent driving; a random choice will be made out of the three available methods: driving to a coordination point, use of a navigation system and being given a series of instructions); ‘productive’ special manoeuvres; questioning by the examiner; self-reflection by the candidate (the candidate will have to fill out an evaluation form before the test and the evaluation form will be used by the examiners after the driving test to explain their decision to the candidate).
In a follow-up study, the new procedure for the driving test will be tested. On the basis of the results of this study the driving test will be adapted and a final version of the new driving test will be delivered from the beginning of 2008. References Barten, M., Drunen, R. van, Herber, N. Jsselstein, S. and Vissers, J.A.M.M. (2005). PROV 2005. Periodiek Regionaal Onderzoek Verkeersveiligheid. [Periodic Road Safety Survey.] Amersfoort, DHV. Baughan, C., Pettersen, N.P., Hendrix, M. and Keskinen, E. (2004). Towards
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European Standards for Testing. Rijswijk, CIECA. Baughan, C.J., Sexton, B., Maycock, G., Simpson, H., Quimby, A. and Chinn, L. (2005). Novice Driver Safety and the British Practical Driving Test. TRL562. Crowthorne, TRL. Engström, I, Gregersen, N.P., Hernetkoski, K., Keskinen, E. and Nyberg, A. (2003). ‘Young novice drivers, driver education and training.’ Literature review. VTIreport 491A. Linköping, VTI. Gregersen, N.P. and Bjurulf, P. (1996). ‘Young novice drivers: towards a model of their accident involvement.’ Accident Analysis and Prevention, 28, 229–41. Hatakka, M., Keskinen, E., Gregersen, N.P., Glad, A. and Hernetkoski, K. (2002). From Control of the Vehicle to Personal Self-Contro: Broadening the Perspectives to Driver Education. Transportation Research Part F, 5, 201–15. Hatakka, M., Keskinen, E., Baughan, C., Goldenbeld, Ch., Gregersen, N.P., Groot, H., Siegrist, S. Willmes-Lenz, G. and Winkelbauer, M. (2003). Basic Driver Training: New Models. Turku, University of Turku. Jonsson, H., Sundström, A. and Henriksson, W. (2003). Curriculum, Driver Education and Driver Testing: A Comparative Study of the Driver Education Systems in Some European Countries. (Educational Measurement No. 44). Umeå University (EM) Department of Educational measurement, Umeå. Kuiken, M.J. and Twisk, D.A.M. (2001). ‘Safe driving and the training of calibration.’ Literature review. Report R-2001-29. Leidschendam, SWOV. Maycock, G., Lockwood, C.R. and Lester, J.F. (1991). The Accident Liability of Car Drivers. Report 315. Crowthorne, Transport Research Laboratory. Maycock, G. and Forsyth, E. (1997). Cohort Study of Learner and Novice Drivers, Part 4: Novice Driver Accidents in Relation to Methods of Learning to Drive, Performance in the Driving Test and Self Assessed Driving Ability Behaviour. Report 275. Crowthorne, Transport Research Laboratory. McDaniel, M.A., Morgeson, F.P., Finnegan, E.B., Campion, M.A. and Braverman, E.P. (2001). ‘Use of situational judgment tests to predict job performance: a clarification of the literature’. Journal of Applied Psychology, 86, 730–40. OECD. (2006) Young Drivers: The Road to Safety. Organisation for Economic Cooperation and Development. Peräaho, M., Keskinen, E and Hatakka, M. (2003). Driver Competence in a Hierarchical Perspective: Implications for Driver Education. Turku, University of Turku/ Traffic Research. Rasmussen, J. (1984). Information Processing and Hum-Machine Interaction: An Approach to Cognitive Engineering. New York/Amsterdam/London, NorthHolland. Sagberg, F. (1998). ‘Month-by-month changes in accident risk among novice drivers.’ Paper presented at the 24th International Congress of Applied Psychology. San Francisco, August 9–14. Siegrist, S. (1999). (ed.) Driver Training, Testing and Licensing: Towards Theorybased Management of Young Drivers’ Injury Risk in Road Traffic. Results of the EU-project GADGET, Work Package 3. Bern, BFU. Stipdonk, H.L., Aarts, L.T., Schoon, C.C. and Wesemann, P. (2006). De essentie in de daling van het aantal verkeersdoden: Ontwikkelingen in 2004 en 2005 en nieuwe
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prognoses voor 2010 en 2020. SWOV Publication R-2006-4. Leidschendam: SWOV. Vissers, J.A.M.M. (2004a). Testing and Teaching of the Higher Order Skills of the GDE-Matrix. CIECA-congress, Warsaw. Vissers, J.A.M.M. (2004b). Modernisering rijexamens, Probleemanalyse beginnende bestuurders. [Modernizing driving certification tests, problem analysis of beginning drivers.] Report number TT04-021. Veenendaal, Traffic Test. Vissers, J.A.M.M. (2006). Lifelong Learning: Education and Training in Schools. CIECA-congress, Marseille. Vissers, J.A.M.M. and Mesken, J. (2007). Modernisering CBR-praktijkexamen. Resultaten deelproeven vernieuwende elementen. [Modernising the Dutch driving test. Results of the pilot studies with new elements for the practical driving test.] DHV, Amersfoort. Vlakveld, W.P. (2005). Jonge beginnende automobilisten, hun ongevalsrisico en maatregelen om dit terug te dringen. Een literatuurstudie. [Young novice car drivers, their accident risk and regulations to reduce this. A literature study.] Leidschendam, SWOV.
Chapter 5
Personality and Attitudinal Predictors of Traffic Offences Among Young Drivers: A Prospective Analysis Lisa Wundersitz and Nicholas Burns University of Adelaide, Australia Introduction In Australia and other developed countries, young drivers (aged 16 to 24 years) represent only a minor proportion of the licensed driving population, yet are substantially more likely to be involved in fatal and injurious crashes than older, more experienced drivers (for example, Legge et al., 2000; Shope et al., 2001). Research suggests that around 90 per cent of crashes are, to some extent, caused by human factors or road user behaviour (Shinar, 1978). As a result, many studies have been undertaken to identify driver characteristics and behaviour associated with crash involvement. In the driving context, personality characteristics and attitudes can influence how individuals approach and behave in certain driving situations. Personality characteristics, by definition, are relatively stable over time; therefore, changing them is not an appropriate goal for young driver countermeasures. However, understanding which personality factors predict driver behaviour might assist in developing interventions and public education matched to the individual needs of young drivers. Moreover, identifying and modifying mediating factors linking personality to risky driving behaviour and crashes may be useful in changing young driver behaviour. Personality characteristics and attitudes have been found to be weakly but consistently associated with young driver crash involvement (for reviews, see Beirness, 1993; Elander et al., 1993). However, the role of personality and attitudes in crash involvement may be underestimated because crashes are relatively rare events. As a result, any differences in crash rates attributed to personality and attitudinal factors will be more difficult to detect statistically (see Evans, 2004 for a discussion). Moreover, crash causation is dependent on factors other than the behaviour of a particular driver, such as environmental circumstances (for example, weather conditions), exposure (for example, annual mileage), and the behaviour of other drivers (Friedstrom et al., 1995; Struckman-Johnson et al., 1989). As crash data lacks stability and statistical power, they are not an ideal measure. A measure based on an aggregate of multiple behaviours might be more appropriate and reliable for examining the influence of personality on behaviour (for example, Epstein, 1979; Ulleberg and Rundmo, 2003). Based on this concept, personality
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and attitudinal measures might be expected to better predict an aggregate measure of risky driving behaviours, such as traffic offences, in comparison to crashes. Driver behaviour captured in traffic offence statistics is predominantly intentional and connected to the characteristics and motivations of the driver (Burg, 1970; Harrington, 1972). An increasing body of research has shown that a variety of personality characteristics and attitudes has a stronger relationship with risky driving or the propensity to commit traffic offences than with crash involvement. From a review of the literature, Beirness (1993) concluded that personality factors accounted for about 10–20 per cent of the variance in crashes and up to 35 per cent of the variance in risky driving. Some of the most prominent personality factors include sensation seeking, mild social deviance, hostility, aggression, and emotional instability (for example, Jonah, 1997; Lawton et al., 1997; Miles and Johnson, 2003; Patil et al., 2006; Trimpop and Kirkcaldy, 1997). With respect to attitudes and behaviours, a risky driving style, the use of driving to reduce tension or stress and a tolerant attitude towards risky driving behaviour have all been associated with young traffic offenders (for example, Baxter et al., 1990; Beirness et al., 1993; Mayer and Treat, 1977; Ulleberg and Rundmo, 2003). Despite the increasing body of research investigating relationships between personality and attitudinal factors and measures of risky driving (that is, traffic offences) among young drivers, there are a number of limitations associated with this research. Firstly, most of these studies were cross-sectional or retrospective in design, whereby the relationship between personality factors and driver behaviour were measured simultaneously or after driving incidents had occurred. A prospective design is advantageous in that personality measures (especially self-reported) can be obtained before being affected by crash involvement. Secondly, these studies primarily relied on self-reported driver behaviour outcomes. Self-reported crash and traffic offence data allows for the possibility of intentional or unintentional misrepresentation (Elander et al., 1993). A final criticism is that many of the studies did not adequately consider the role of driving exposure. Generally, driving exposure varies with age (Massie et al., 1997). However, there can also be considerable variation in the level of driving exposure and travel patterns within different age groups. This is because driving exposure is not a random factor but an individual choice. Driving exposure has been found to vary among young drivers by factors such as sex and motivation for driving (Crettenden et al., 1994; Gregersen and Berg, 1994; Massie et al., 1997). Thus, while personality and attitudes may influence the way in which an individual chooses to drive, reflected in traffic offences, it may also influence how much an individual drives (quantity of driving exposure). For example, drivers with high levels of sensation seeking might choose to drive more frequently to experience feelings of excitement or drivers with emotional problems or high levels of hostility may choose to drive more frequently to release feelings of tension or stress. The aim of the present study was to identify personality and attitudinal factors that predict subsequent traffic offences, recorded in official driver records, among young drivers. This study also investigated whether any personality and attitudinal factors predicted different levels of driving exposure, defined as self-reported kilometres
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driven. This study contributes to past research on this topic by being the first study, to the best of our knowledge, to use a prospective design and official records to examine the role of personality and attitudinal factors among young drivers. Method Participants The original sample consisted of 336 young drivers. A total of 128 drivers were excluded from the study because they did not consent to the release of their driver records. Consequently, the final sample consisted of 208 young drivers (169 males, 39 females) aged 16 to 24 years (M = 18.5, SD = 1.2). Participants were recruited from the Driver Intervention Programme, a small-group discussion-based workshop for drivers aged 25 years and under who have violated the conditions of their learner’s permit or provisional licence, resulting in licence disqualification. Thus, by definition, all participants recorded a traffic offence prior to participation in the study. Participants were required to hold a current South Australian provisional driver’s licence ensuring all had some unsupervised driving experience. Participants had held a provisional licence for an average of 1.4 years (sd = 0.94) prior to questionnaire administration. Ethical approval was obtained for this study. Questionnaire Participants completed an extensive self-report questionnaire consisting of 136 items. The measures included in this questionnaire were selected for their known association with risky driving and crash involvement in the literature and primarily for the purposes of another study (see Wundersitz and Burns, 2005). The questionnaire took approximately ten to fifteen minutes to complete. The first part of the questionnaire sought information on a number of general demographic, licensing, and background variables including driving exposure (estimated number of kilometres driven). The second section incorporated 72 true or false items measuring general personality traits: assertiveness (five items: Rathus, 1973), depression (mood rather than clinical symptoms; nine items: Costello and Comrey, 1967), emotional adjustment (six items: Howarth, 1976) and sensation seeking (ten items from the Thrill and Adventure Seeking scale and seven items from the Disinhibition scale; Zuckerman, 1971). In addition, five measures of the expression of hostility or aggression were included (Buss and Durkee, 1957): assaultiveness (nine items), indirect hostility (five items), verbal hostility (nine items), irritability (eight items) and resentment (four items). A further 20 true or false items measured a variety of driving-related attitudes and behaviours: driving aggression (ten items: Parry, 1968), an attitude of competitive speed (five items: Goldstein and Mosel, 1958), driving inhibition (cautious driving when upset or angry; three items: Donovan and Marlatt, 1982) and the extent to which driving
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reduced tension (two items: Mayer and Treat, 1977; Pelz and Schuman, 1971). In following sections, a measure of mild social deviance (eight items: West et al., 1993), self-reported driving style or risky driving (seven items: Deery and Love, 1996) and eight separate items measuring specific driving attitudes were also incorporated, as was alcohol consumption, which is another measure of high-risk behaviour. The internal consistency of these measures has been established in other research (see Wundersitz and Hutchinson, 2006). Official driver records To obtain official traffic offence records, participants provided their driver’s licence number. Driver licence numbers were used to search the DRIVERS database for traffic offences detected by police on South Australian roads. DRIVERS does not include infringements from speed cameras; thus, the number of traffic offences recorded was certainly an underestimate of the true number of offences. The traffic offence records of participants were tracked for 12 months following questionnaire administration. It is acknowledged that some drivers were disqualified for part of this period. Statistical analysis Statistical analyses were performed to determine if young drivers recording subsequent traffic offences possessed certain personality characteristics and attitudes. For univariate analyses, chi-square tests were conducted for categorical variables and independent samples t-tests were conducted for continuous variables. Note that if the assumption of normally distributed data was violated, t-tests were performed using Welch’s procedure because it does not assume equal population variances, making the t-test more robust. Cohen’s d, a standardised measure of the effect size or strength of the difference between means, was reported for t-tests with significant results. According to Cohen’s guidelines (Cohen, 1988), an effect size of d = 0.2 represents a small effect, d = 0.5 a medium effect and d = 0.8 a large effect. Binary logistic regression was conducted for the multivariate analysis. Logistic regression does not make any assumptions about the statistical distribution of individual drivers’ traffic offence frequency. Results Examination of official driver records showed that 38 per cent (n = 80) of young drivers were detected committing at least one traffic offence during the 12 month period following questionnaire administration. The distribution of the number of traffic offences is shown in Figure 5.1. Just over 14 per cent (n = 30) of young drivers recorded two or more traffic offences in the following year.
Personality and Attitudinal Predictors of Traffic Offences
Figure 5.1
55
Distribution of the number of traffic offences recorded after questionnaire administration
Table 5.1 shows the demographic characteristics and driving exposure of young drivers with and without a recorded traffic offence during the 12 month follow up period. Drivers recording a traffic offence were statistically significantly more likely to be male (43 per cent) than female (21 per cent) (χ2(1) = 6.5, p = 0.011). Traffic offence status was not related to age. Driving exposure was measured in terms of the estimated number of kilometres driven per year. Young drivers recording at least one traffic offence reported driving more kilometres per year than drivers without a traffic offence (t(100) = 2.7, p = 0.007).
Table 5.1
Background variables for young drivers recording and not recording a subsequent traffic offence
Variables Sex (%) Males Females Age (years) (sd) Kilometres driven per year (sd)
None
At least one
p-value
42.6 20.5 18.6 (1.4) 21,364.5 (19,633.8)
0.011
79.5 18.4 (1.1) 14,172.8 (12,136.8)
0.205 0.007
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Kilometres driven per year (driving exposure) Personality characteristics and attitudes may influence driving exposure or the number of kilometres driven per year. To investigate this possibility, a linear regression procedure was performed with kilometres driven per year as the dependent variable and all other personality and attitude measures, including sex, as predictor variables. A stepwise procedure was used for the analysis, with the level of significance required for entry into the equation set at p < 0.05. The results of this procedure, summarised in Table 5.2, indicated that a risky driving style and the use of driving to reduce tension were the two best predictors of kilometres driven per year. However, the model featuring these two variables accounted for only 7 per cent of the variance (Adjusted R squared). Table 5.2
Results of a linear regression predicting kilometres driven per year, using personality and attitudinal measures as predictors (N = 179)
Variables in model (order of entry)
B
Adj R2
Risky driving style 494.68 0.05 Tension reduction 3129.40 0.07 Note: Final model F(178) = 7.97, p < 0.001
β
t
p-value
0.19 0.17
2.61 2.35
0.010 0.020
Traffic offences To determine if young drivers recording subsequent traffic offences were characterised by certain personality measures and attitudes, their mean scores on such measures were compared to drivers who did not record a traffic offence (see Table 5.3). There were no statistically meaningful differences in the means of personality measures for drivers with and without traffic offences. Analysis of hostility measures indicated drivers recording a traffic offence had higher levels of assaultiveness (d = 0.32) but lower levels of indirect hostility (d = 0.28) than drivers without traffic offences. The effect sizes indicate that these differences were small. With respect to the driving-related measures, several differences were found; drivers recording a traffic offence reported higher levels of competitive speed, used driving to reduce tension, and had a riskier driving style. The corresponding effect sizes for these measures were in the small to medium range (d = 0.44, d = 0.38, d = 0.39, respectively). The attitudinal measures, specific to road safety, suggested that drivers recording a traffic offence thought speeding was acceptable (d = 0.32). To determine whether any personality characteristics or attitudes predicted subsequent traffic offences, all measures that differed by traffic offence record in univariate analyses were entered into a logistic regression (dependent variable: at least one offence or no offences). Sex and kilometres driven per year were also
Personality and Attitudinal Predictors of Traffic Offences
Table 5.3
57
Mean scores on selected personality and attitudinal measures for drivers recording subsequent traffic offences and no subsequent traffic offences (N = 208) At least one offence (n = 80)
No offences (n = 128)
Measure Mean SD Mean SD t-value df p-value Personality Assertiveness 7.94 1.32 7.83 1.33 0.58 206 0.563 Depression 10.48 1.84 10.05 1.81 1.62 206 0.107 Emotional 7.49 1.49 7.49 1.55 0.02 206 0.983 adjustment Sensation seeking 26.81 3.28 26.67 3.52 0.29 206 0.774 Mild social 12.29 2.96 11.80 2.86 1.19 206 0.236 deviance Hostility and aggression Assaultiveness 13.85 1.96 13.18 2.22 2.22 206 0.028 Indirect hostility 7.59 1.29 7.97 1.36 2.01 206 0.046 Verbal hostility 13.86 1.71 13.68 1.82 0.72 206 0.472 Irritability 11.44 1.71 11.25 2.01 0.69 206 0.489 Resentment 5.66 1.21 5.42 1.12 1.46 206 0.146 Driving-related Aggression 13.30 2.52 12.92 2.60 1.03 206 0.303 Competitive speed 7.74 1.62 7.00 1.72 3.08 206 0.002 Inhibition 4.25 1.15 4.55 1.13 1.88 206 0.062 Tension reduction 3.44 0.76 3.11 0.92 2.80 189 0.006 Risky driving style 20.27 6.10 17.87 6.19 2.75 206 0.007 Attitudes1 Speeding 2.93 1.29 2.52 1.29 2.18 206 0.030 acceptable Drink driving 2.45 1.56 2.59 1.72 0.62 180 0.535 acceptable Low risk of dying 1.88 1.16 1.82 1.18 0.33 206 0.744 in crash Friends don’t drive 3.14 1.28 3.13 1.18 0.07 206 0.943 safely Low likelihood of 2.35 1.20 2.44 1.31 0.48 206 0.629 being caught Lack of concern 1.25 1.61 1.17 1.34 206 0.183 for hurting others Poor driving skill 2.03 1.07 2.05 1.01 0.20 206 0.840 Low safety 2.09 1.07 2.11 1.05 0.15 206 0.885 motivation Note: For each measure, higher scores indicate higher levels of the variable, except for emotional adjustment where higher scores indicate lower levels of adjustment. 1 For each attitude measure, higher scores indicate non-safety orientated attitudes.
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included as predictor variables because group differences were found in univariate analyses. The results from the logistic regression, presented in Table 5.4, show that the probability of recording at least one subsequent traffic offence increased with higher levels of driving to reduce tension, independent of kilometres driven. The odds ratio indicated that drivers who used driving to reduce tension were 1.5 times as likely to record a subsequent traffic offence. No other personality measures entered into the logistic regression predicted traffic offences. Table 5.4
Measure
Results of logistic regression analysis for predicting at least one subsequent traffic offence, using personality and attitude measures as predictors (N = 179) B
SE
Wald
p-value
Tension reduction 0.42 0.19 4.80 0.028 Kilometres driven per year < 0.01 < 0.01 5.34 0.021 Note: Data for kilometres driven were missing for 29 participants.
Odds ratio
95% CI
1.52 1.00
1.05–2.21 1.00–1.00
A positive regression coefficient for kilometres driven indicates that the probability of recording a traffic offence increased with the greater number of kilometres driven. This model was statistically significant (χ2(2) = 13.8, p = 0.001). Discussion Understanding which personality factors and attitudes predict driver behaviour might assist in matching interventions to the individual needs of young drivers. Thus, the primary purpose of the present study was to identify personality and attitudinal factors associated with traffic offences among young drivers using official driver records. By using a prospective design, we attempted to minimise the effects of any crash experience on self-reported measures of individual differences. Self-reported measures provide an opportunity for drivers to give a ‘good’ or socially desirable account of themselves, particularly when the traits and behaviours assessed are undesirable (for example, hostility). Based on the findings from this study, a flow chart showing the predictors of traffic offences, incorporating kilometres driven per year, is depicted in Figure 5.2. The results of this study showed that traffic offences, serving as a proxy for crashes, were predicted by kilometres driven per year and a driving-related behavioural measure, driving to reduce tension. The fact that relationships between variables and traffic offences reported in official records were found is notable given that the offences reported in driver records are relatively rare and under-represent the actual number of risky driving behaviours performed (that is, they contain only the number of times a driver was detected offending).
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Risky driving style
Driving to reduce tension
Kilometres driven per year
Traffic offences
Figure 5.2 Flow chart of predictors of traffic offences for young drivers In contrast to previous research using self-reported driving behaviour, none of the specific personality variables were associated with traffic offences. Although there are many advantages of using official driver records over self-reported data, there are some limitations associated with official records that affect their ability to detect relationships. Zylman (1972) argued that research based solely on official driver records may yield spurious results and, in many cases, non-significant results because the likelihood of recording a crash or traffic offence may be more dependent on local policies and practices than the driver’s proficiency or driving behaviour. Moreover, not all traffic offences are enforced equally and this may bias the data such that some groups of drivers are over-represented (Smiley et al., 1991). An interesting finding of this study was that using driving to reduce tension predicted kilometres driven per year and traffic offences (independent of kilometres driven per year). Social learning theory suggests that if an individual has not learnt sufficient means of coping with tension or frustrations, driving may be used as a way of venting these feelings (Grey et al., 1989). The findings of the present study are consistent with past studies that found the use of driving to release tension was associated with traffic offences and crash involvement, particularly among males (Donovan et al., 1985; Harano et al., 1975; Mayer and Treat, 1977). The use of driving to reduce tension is not a personality trait but a behavioural manifestation of such traits in the driving context that has been learned and is, therefore, more amenable to change. Consequently, it may be beneficial to develop interventions or public education for young drivers that highlight the importance of using effective strategies to deal with feelings of tension or stress, other than on the road. The finding that kilometres driven per year predicted traffic offences is consistent with previous research (for example, Trimpop and Kirkcaldy, 1997). High levels of driving exposure or kilometres driven has consistently been correlated with traffic offences because greater driving exposure allows greater opportunity to commit, and be detected committing, a traffic offence. This finding reinforces the view that kilometres driven should be included as a covariate when examining factors associated with driver behaviour. However, note that the measure of driving exposure in this study was based on self-reported estimates of kilometres driven. By
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nature, self-reported data are unreliable because they are subject to poor recall and misrepresentation. The association between risky driving style (that is, the manner in which one chooses to drive), the use of driving to reduce tension and kilometres driven per year suggests greater distance driven represents, to a small extent, unsafe or risky motives for driving. This notion is consistent with some previous research. For example, Gregersen and Berg (1994) found that a group of high-risk young drivers that reported more driving than other groups were characterised by an interest in cars, being ‘out and about’ and driving for extra motives other than transport. Similar to the present study, the majority of the group were male (about 80 per cent). Alternatively, young drivers’ risky driving style and resultant higher kilometres driven per year may serve purposes associated with adolescent development not examined in this study, such as opposing authority, asserting independence and impressing peers (Jessor et al., 1997). Future research could explore the nature of this interesting relationship. Nevertheless, whether kilometres driven is an expression of masculinity, an interest in cars or a claim to adulthood, the modification of these motivations for driving might reduce the amount of kilometres driven by young drivers, resulting in crash reductions. The prospective design of this study provides an opportunity to continue following the driver records of these young drivers for two to three years to accumulate a greater number of recorded traffic incidents (but bearing in mind that crash risk is not stable and varies with age). A prospective examination of the characteristics of young drivers detected for several offences or crashes would provide a convincing means of understanding the role of personality characteristics and attitudes in driver behaviour among young drivers. Consequently, information from this research would be valuable in further developing and tailoring interventions to the individual needs of young drivers. Acknowledgements I wish to thank Dr Paul Hutchinson for providing useful comments on this paper. References Baxter, J.S., Manstead, A.S.R., Stradling, S.G., Campbell, K.A., Reason, J.T. and Parker, D. (1990). ‘Social facilitation and driver behaviour.’ British Journal of Psychology, 81: 351–60. Beirness, D.J. (1993). ‘Do we really drive as we live? The role of personality factors in road crashes.’ Alcohol, Drugs and Driving, 9(3–4): 129–43. Beirness, D.J., Simpson, H.M. and Mayhew, D.R. (1993). ‘Predicting crash involvement among young drivers.’ In H.D. Utzelmann, G. Berghaus and G. Kroj (eds). Proceedings of the International Conference on Alcohol, Drugs and Traffic Safety. Cologne: Verlag TUV Rhineland. Burg, A. (1970). ‘The stability of driving record over time.’ Accident Analysis and
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Prevention, 2: 57–65. Buss, A.H. and Durkee, A. (1957). ‘An inventory for assessing different kinds of hostility.’ Journal of Consulting and Clinical Psychology, 21: 343–9. Cohen, J. (1988). Statistical Power Analysis for the Behavioural Sciences (2nd ed.). Hillsdale, NJ: Erlbaum. Costello, C.G. and Comrey, A.L. (1967). ‘Scales for measuring depression and anxiety.’ Journal of Psychology, 66: 303–13. Crettenden, A.V., Yeo, E.-Y. and Drummond, A.E. (1994). Qualitative Dimensions of Young Driver Driving Exposure as a Function of Time of Day (No. CR 148). Canberra: Federal Office of Road Safety. Deery, H.A. and Love, A.W. (1996). ‘The driving expectancy questionnaire: development, psychometric assessment and predictive utility among young drinkdrivers.’ Journal of Studies on Alcohol, 57: 193–202. Donovan, D.M. and Marlatt, G.A. (1982). ‘Personality subtypes among drivingwhile-intoxicated offenders: relationship to drinking behaviour and driving risk.’ Journal of Consulting and Clinical Psychology, 50(2): 241–9. Donovan, D.M., Queisser, H.R., Salzburg, P.M. and Umlauf, R.L. (1985). ‘Intoxicated and bad drivers: subgroups within the same population of high risk men drivers.’ Journal of Studies on Alcohol, 46: 375–82. Elander, J., West, R. and French, D. (1993). ‘Behavioral correlates of individual differences in road-traffic crash risk: an examination of methods and findings.’ Psychological Bulletin, 113(2): 279–94. Epstein, S. (1979). ‘The stability of behaviour: on predicting most of the people much of the time.’ Journal of Personality and Social Psychology, 37: 1097–126. Evans, L. (2004). Traffic Safety. Bloomfield Hills, MI: Science Serving Society. Friedstrom, L., Ifver, J., Ingebrigtsen, S., Kulmala, R. and Thomsen, L.K. (1995). ‘Measuring the contribution of randomness, exposure, weather and daylight to the variation in road accident count.’ Accident Analysis and Prevention, 27: 1–20. Goldstein, L.G. and Mosel, J.N. (1958). ‘A factor study of drivers’ attitudes, with further study on driver aggression.’ Highway Research Board Bulletin, 172: 9–29. Gregersen, N.P. and Berg, H.Y. (1994). ‘Lifestyle and accidents among young drivers.’ Accident Analysis and Prevention, 26: 297–303. Grey, E.M., Triggs, T.J. and Haworth, N.L. (1989). Driver Aggression: The Role of Personality, Social Characteristics, Risk and Motivation. (No. CR 81). Clayton, Victoria: Monash University. Harano, R.M., Peck, R.C. and McBride, R.S. (1975). ‘The prediction of accident liability through biographical data and psychometric tests.’ Journal of Safety Research, 7: 16–52. Harrington, D.M. (1972). ‘The young driver follow-up study: an evaluation of the role of human factors in the first four years of driving.’ Accident Analysis and Prevention, 4: 191–240. Howarth, E. (1976). ‘A psychometric investigation of Eysenck’s personality inventory.’ Journal of Personality Assessment, 40(2): 173–85. Jessor, R., Turbin, M.S. and Costa, F.M. (1997). ‘Predicting developmental change in risky driving: the transition to young adulthood.’ Applied Developmental Science,
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1(1): 4–16. Jonah, B. A. (1997). ‘Sensation seeking and risky driving.’ In T. Rothengatter and E. Carbonell Vaya (eds). Traffic and Transport Psychology – Theory and Application. Great Britain: Pergamon: 259–67. Lawton, R., Parker, D., Stradling, S.G. and Manstead, A.S.R. (1997). ‘Predicting road traffic accidents: the role of social deviance and violations.’ British Journal of Psychology, 88(2): 249–62. Legge, M., Kirov, C. and Cercarelli, R. (2000). Reported Road Crashes in Western Australia. Perth: Road Safety Council of Western Australia 2000. Massie, D.L., Green, P.E. and Campbell, K.L. (1997). ‘Crash involvement rates by driver gender and the role of average annual mileage.’ Accident Analysis and Prevention, 29(5): 675–85. Mayer, R. and Treat, J. (1977). ‘Psychological, social and cognitive characteristics of high-risk drivers: a pilot study.’ Accident Analysis and Prevention, 9(1): 1–8. Miles, D.E. and Johnson, G.L. (2003). ‘Aggressive driving behaviors: are there psychological and attitudinal predictors?’ Transport Research Part F, 6(2): 147– 61. Parry, M.H. (1968). Aggression on the Road. London: Tavistock Press. Patil, S.M., Shope, J.T., Raghunathan, T.E. and Bingham, C.R. (2006). ‘The role of personality characteristics in young adult driving.’ Traffic Injury Prevention (7). Pelz, D.C. and Schuman, S.H. (1971). ‘Are young drivers really more dangerous after controlling for exposure and experience?’ Journal of Safety Research, 3(2): 68–79. Rathus, S.A. (1973). ‘A 30 item schedule for assessing assertive behavior.’ Behavior Therapy, 4: 398–406. Shinar, D. (1978). Psychology on the Road – The Human Factor in Traffic Safety. New York: Wiley. Shope, J.T., Waller, P.F., Raghunathan, T.E. and Patil, S.M. (2001). ‘Adolescent antecedents of high-risk driving behaviour into young adulthood: substance use and parental influences.’ Accident Analysis and Prevention, 33(5): 649–58. Smiley, A., Hauer, E., Persaud, B., Clifford, L. and Duncan, D. (1991). Accident Potential: An Ontario Driver Records Study Summary Report. Ontario: Ministry of Transportation. Struckman-Johnson, D.L., Lund, A.K., Williams, A.F. and Osborne, D.W. (1989). ‘Comparative effects of driver improvement programs on crashes and violations.’ Accident Analysis and Prevention, 21(3): 203–15. Trimpop, R. and Kirkcaldy, B. (1997). ‘Personality predictors of driving accidents.’ Personality and Individual Differences, 23(1): 147–52. Ulleberg, P. and Rundmo, T. (2003). ‘Personality, attitudes and risk perception as predictors of risky driving behaviour among young drivers.’ Safety Science, 41: 427–43. West, R., Elander, J. and French, D. (1993). ‘Mild social deviance, type-a behaviour pattern and decision-making style as predictors of self-reported driving style and traffic accident risk.’ British Journal of Psychology, 84: 207–19. Wundersitz, L.N. and Burns, N. (2005). ‘Identifying young driver subtypes: relationship to risky driving and crash involvement.’ In L. Dorn (ed.). Driver
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Behaviour and Training (vol. II: 155–68). Aldershot, UK: Ashgate. Wundersitz, L.N. and Hutchinson, T.P. (2006). South Australia’s Driver Intervention Program: A Discussion of Best Practice, With Data on Characteristics of the Participants and a Selective Literature Review (No. CASR021). Adelaide, South Australia: Centre for Automotive Safety Research. Zuckerman, M. (1971). ‘Dimensions of sensation seeking.’ Journal of Consulting and Clinical Psychology, 36: 45–52. Zylman, R. (1972). ‘Drivers’ records: are they a valid measure of driving behavior?’ Accident Analysis and Prevention, 4: 333–49.
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Chapter 6
Pre-driving Attitudes and Non-driving Road User Behaviours: Does the Past Predict Future Driving Behaviour? Helen N. Mann1 and Mark J.M. Sullman2 1 Heriot-Watt University, UK 2 Hertfordshire University, UK Introduction The number of fatalities and injuries on our roads continues to increase at an alarming rate. However, this is not just a problem for Britain and Europe, it is a global concern. It is estimated that every year 1.2 million people die in road accidents worldwide and somewhere in the region of 20–50 million people are known to be injured or permanently disabled (WHO, 2004; WHO, 2007). According to projections made by the World Health Organisation (WHO), these figures are likely to increase by about 65 per cent over the next 20 years unless preventative action is taken (WHO, 2004). Over the past four years road safety has become a priority for Government agencies around the world. In 2004, the WHO for the first time ever selected road safety as the topic for World Health Day (WHD). The aim of this day was to raise global awareness about the magnitude, risk factors and impacts of road traffic accidents. More recently the United Nations (UN) brought road safety back into the media spotlight by declaring 23–29 April 2007 Global Road Safety Week (GRSW). The focal point for the GRSW was specifically the problems that face young road users, unlike the WHD, which looked at general road safety. It is estimated that just over 1000 young people are killed every day in road traffic collisions worldwide and that more than 30 per cent of all people killed and seriously injured in road accidents are under 25 years (WHO, 2007). With the number of young people involved in road traffic accidents on the increase, it is unsurprising to learn that road traffic injuries have been ranked as the eighth leading cause of global deaths among young people (WHO, 2007). Several factors are thought to affect the vulnerability of young pedestrians, cyclists and drivers, with some of the main issues relating to development, social influence and personality. Developmental factors can affect young road users’ safety on the road in many different ways. Firstly, children and adolescents are often smaller in stature than adults, thus drivers do not always see them; secondly, their perceptive skills have not yet fully developed. Under-developed depth perception can, for example, impair an individual’s ability to accurately judge the distance between themselves and objects in their environment and immature auditory perception can impair their
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ability to accurately judge from the sounds of their engines the direction and size of approaching vehicles. Finally, young road users often have a limited attention span which can mean that they are often easily distracted, finding it difficult to attend to more than one problem simultaneously (WHO, 2007). These are of course important skills to master in order to navigate the road environment safely. Social factors such as peer and parental pressure can also have an impact on road user behaviour. Adolescent pedestrians and cyclists have reported that the more they go out with their friends the less often they carry out desirable road user behaviours (Elliott and Baughan, 2003). Those who report being influenced more by their parents than their peers have been shown to display more desirable behaviours on the road (Elliott and Baughan, 2003; Jessor, Turbin and Costa, 1997). For example, young drivers who report being influenced more by their parents also report less risky driving behaviour (Jessor, Turbin and Costa, 1997). Personality factors such as risk-taking (or sensation seeking) have also been linked to risky road user behaviour and poor road safety attitudes. People with a high preference for risk-taking have been found to report more negative attitudes to road safety and more frequent occurrences of risk-taking whilst driving (Iversen, 2004; Ulleberg and Rundmo, 2003). Among pre-drivers, sensation seeking and adherence to social values were associated with risky road user behaviour (West, Train, Junger, Pickering, Taylor and West, 1998). Adolescents who report high sensation seeking and low adherence to social values were those who also reported riskier behaviour on the roads. In addition to links between sensation seeking and risky driving or road user behaviour, Arnett (1994) has reported a correlation between sensation seeking and several other risky behaviours in adolescents, such as drug-taking. In social psychology it has been postulated that problem behaviours are linked to form a ‘syndrome’ of behaviours that characterise adolescence (Jessor, 1987), known as the Problem Behaviour Theory (PBT) and Arnett’s (1994) results would appear to provide supportive evidence for this theory. Support of Jessor’s theory has also been lent by the results of a study conducted in Australia by Wundersitz and Burns (2005). This research looked at personality characteristics that defined highrisk young drivers and discovered that there were four different driver sub-types. The sub-type of drivers with the highest road safety risk were characterised by high levels of driving-related aggression, competitive speed, driving to reduce tension or increase personal efficacy and assertiveness. The results also reported positive scores on sensation-seeking, positive attitudes towards speeding and that they engaged in other high-risk behaviours such as drinking large quantities of alcohol. Risky driving is not the only form of road user behaviour that has been linked to engagement in other forms of problem behaviour. West et al. (1998) provided evidence to suggest that there was an additional link between problem behaviour and pedestrian behaviour. These results reported the existence of a significant relationship between engagement in problem behaviour (in the form of risk-taking) and increased pedestrian accident involvement. Having identified these links between problem behaviour, risky road user behaviour and high accident risk, the question arises as to whether or not there is any linkage contained within them. In an effort to understand the young driver problem and to aid the development of future road safety initiatives, this research aims
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to establish a link between adolescents’ pre-driver road user behaviour, attitudes and future driving behaviour. It also aims to identify both pre-driver and driver characteristics that predict risky driving behaviour. Providing the research aims are met, the necessity for the designs of pre-driver interventions which create safe attitudes to both driving and general road safety will be emphasised. Method Participants This is the second in a two-part longitudinal study conducted in New Zealand. Four hundred and seventy-one students (196 males, 275 females) aged 14–16 years (mean age 15 years) took part and fully completed both parts of the study. All participants were non-drivers at Time 1, but by Time 2 there were 208 drivers. When investigating the existence of a link between pre-driver attitudes and behaviour with post-driver attitudes and behaviour, the questionnaires were filtered so that only the responses of participants who were drivers by Time 2 were analysed. Out of the 208 drivers, only 195 (97 males, 98 females) had fully completed the Driver Behaviour Questionnaire (DBQ), which was the main source of information regarding their driving behaviour. Therefore, statistical analysis was conducted only on the responses of the 195 participants aged 15–16 (M = 15.1yrs, SD = 0.24). Participants were from 29 schools across New Zealand (ten South Island schools, 19 North Island schools). They were selected to participate in the study at random by teachers within their schools. Questionnaires This longitudinal study looks at individuals’ responses to two questionnaires completed approximately twelve months apart, using several different scales of measurement. The first questionnaire consisted of three sections, including items from: the Adolescent Road User Behaviour Questionnaire (ARBQ), the Theory of Planned Behaviour applied to speeding (referred to as TPB-speeding), two of the Driver Attitudes Questionnaire (DAQ) subscales and one new DAQ subscale designed in this study to measure attitudes to seat belts. Adolescent road user behaviour was measured using the Adolescent Road User Behaviour Questionnaire (ARBQ), designed by Elliott and Baughan (2003, 2004). The scale measured three types of road user behaviour: unsafe road crossing behaviour, play and social activity on the road, and engagement in protective behaviour on the road. Responses were made on a five point Likert scale where 1 = ‘never’ and 5 = ‘very often’. To ensure that increasing scores on all three sub-scales were indicative of high risk behaviour, the items measuring protective behaviour on the road were reversed and re-labelled ‘non-engagement of protective behaviour on the road’ (that is, the higher the score, the less protective behaviour they engage in). Dangerous road users in this study were therefore those participants who scored high on each
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of the three subscales. The original ARBQ was composed of a 43 item scale but, for the purposes of this study, the reduced 23 item scale was used (as recommended by the authors of the study). Components of the TPB-speeding were examined to try and identify the attitudes and intentions of pre-drivers to speeding in the future. The extended TPB scale designed by Parker, Manstead and Stradling (1995) was used to measure components of the original TPB. They broadened the TPB model to include a new measure of personal norm, which included items on moral norm and anticipated regret. As participants at Time 1 were non-drivers, direct measures as opposed to beliefbased measures were used; that is, they could not be asked about control beliefs and power beliefs regarding driving behaviour. In total there were ten questions measuring 13 items (one moral norm question, two anticipated regret questions, one attitude question with four parts to it, two subjective norm questions, three perceived behavioural control questions and one measure of intention were used). Responses were made on a seven point Likert scale where 1 = ‘strongly agree’ and 7 = ‘strongly disagree’. The Driver Attitude Questionnaire (DAQ) was designed to measure attitudes to bad driving practices, such as speeding and drink-driving (Parker, Manstead, Stradling and Senior, 1998). The original DAQ consisted of four sub-sections, with ten items in each measuring attitudes towards: drink-driving, speeding, over-taking and close following. In this study there were 22 questions (nine speeding items, eight drink-driving items and five seat belt items), 17 of these measured attitudes to speeding and drink-driving (16 being taken from Parker, Manstead, Stradling and Senior’s (1998) Driver Attitude Questionnaire [DAQ]). In the DAQ there were ten items on drink-driving, however in this study only seven were chosen (three items measuring attitudes towards breath-testing and knowledge of the legal blood alcohol limit were removed because it was assumed that as participants were under the legal drinking age they may not be aware of alcohol limits and laws). A new item, ‘it’s ok to drink and drive’ was added, as it was felt that pre-drivers would be able to respond. Similarly, there were ten items on speeding in the original DAQ but only nine were chosen for this study (the item ‘I know exactly how fast I can drive and still drive safely’ was omitted because it was considered that it was not applicable to pre-drivers). As well as the two subscales measuring speeding and drink-driving, a third subscale, consisting of five items, was created to measure attitudes towards not wearing seat belts. Some of the questions used in the other two sections on speeding and drink-driving were re-worded so that they could be applied to attitudes to not wearing seat belts. Responses were made on a five point Likert scale where 1 = ‘strongly agree’ and 5 = ‘strongly disagree’. The second questionnaire consisted of three sections using eight items from the first questionnaire, Arnett’s Inventory of Sensation Seeking (AISS) and the Driver Behaviour Questionnaire (DBQ). To establish whether attitudes and intentions changed during the intervening year’s period, as the adolescents went from being pre-drivers to drivers, eight items from the first questionnaire were repeated in the second questionnaire. Four of the items that measured attitudes to speeding were included (responses to the four items
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were added together and averaged), along with one item on speeding intention. Three items from the DAQ measuring attitude towards speeding, drink-driving and non-use of seat belts were also included. Arnett’s Inventory of Sensation Seeking (AISS) was designed to measure sensation seeking level (Arnett, 1994); the AISS scale consisted of 20 items in total, further sub-divided into two subscales, namely Novelty (the need to seek novel stimulation) and Intensity (the need to seek intense experiences). Both subscales, containing ten items each, were aggregated to find a total score. Responses were made on a four point Likert scale where 1 = ‘does not describe me at all’ and 5 = ‘describes me very well’. High scores on both the sub-scales and on total AISS score (the combined Novelty and Intensity scores) were indicative of high sensation seekers. The DBQ was designed to measure the frequency of drivers’ self-reported behaviour on the road. There are several different versions of the scale, but the version chosen for this study consisted of 24 items measuring driving errors, lapses and violations (all violations were Highway Code violations, aggressive violations were not included). Responses were measured on a six point Likert scale where 0 = ‘never’ and 5 = ‘all the time’. Results This study focuses on the results from the second questionnaire and the data collected from those subjects in the longitudinal study who had become drivers, tests comparing responses from non-drivers and drivers at Time 2 were also conducted. The data were analysed using t-tests, ANOVAs and correlations. Regressions were also run which incorporated items from both questionnaires, in order to establish whether or not a relationship existed between self-reported driving behaviour and attitudes at Time 2 and pre-driver road user behaviour and attitudes at Time 1. The results showed that there were significant sex differences on the second questionnaire, but there were no significant differences according to the location of participants (North or South Island) or the area they lived in (City/town/rural). Significant sex differences were found on both the AISS sensation seeking scale and the DBQ self-reported driving behaviour scale. Males scored significantly higher than females on overall Sensation Seeking score (t (193) = 3.39, p < 0.001) and on Intensity (t (193) = 4.55, p < 0.001). Females, however, scored significantly higher than males on driving errors (t (193) = –2.39, p < 0.05) and lapses (t (178) = –3.46, p < 0.001). There were no significant sex differences regarding the eight TPB speeding items asked about in both questionnaires. Correlations that were run on items from the second questionnaire revealed that driving violations were significantly correlated with AISS Sensation Seeking scores (r = 0.29, p < 0.001), Intensity scores (r = 0.21, p < 0.05) and Novelty scores (r = 0.26, p < 0.001). Lapses were also significantly correlated with Novelty (r = 0.17, p < 0.05). A correlation matrix of all items from both questionnaires revealed that, with regard to the three DAQ questions about attitudes (to speeding, drink-driving and
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not wearing seat belts), there were correlations with Intensity on the AISS, play and social activity on the road on the ARBQ, non-engagement in protective behaviour also on the ARBQ and scores on some of the TPB-speeding items (speeding moral norm, anticipated regret, attitude and subjective norm). DAQ attitudes to speeding and drink-driving scores also correlated with scores on the DBQ violations subscale, the ARBQ unsafe road crossing subscale and the TPB speeding perceived behavioural control and speeding intention items. The correlation also showed that scores on speeding intention at Time 1 (predriving) correlated with scores on driving violations on the DBQ (r = 0.28, p < 0.001) and scores on TPB speeding intention at Time 2 (post-driving) (r = 0.30, p < 0.001). Speeding intention at Time 2 (post-driving) also correlated with scores on driving violations on the DBQ (r = 0.44, p < 0.001), driving errors on the DBQ (r = 0.21, p < 0.05), overall sensation seeking on the ARBQ (r = 0.24, p < 0.001), AISS Intensity (r = 0.20, p < 0.05) and Novelty (r = 0.20, p < 0.05). Three regressions were run to find those questionnaire items that significantly predicted scores on DBQ errors, lapses and violations from both Time 1 and Time 2. The best predictors for driving lapses were sex and score on the ARBQ play and social activity on the road subscale (measured at Time 1, pre-driving), which were factors that were also the best predictors of driving errors. The regression to predict driving violations, however, revealed that scores on the ARBQ subscales of unsafe road crossing behaviour and play and social activity on the road (again measured at Time 1, pre-driving) were the best predictors. A fourth regression was run on the data to look at the effect of age, sex and predriving attitudes (attitudes to drink-driving, speeding and not wearing seat belts), measured at Time 1 pre-driving, and at predicting driving violation scores at Time 2. The results showed that the only significant predictor of driving violation scores were scores on attitudes to speeding. To see if there was a significant difference in response between Time 1 and Time 2, paired samples t-tests were run on the data collected from the eight TPB-speeding questions that were asked in both questionnaires. The first statistical analysis that was run focused on responses collected from all participants who completed both questionnaires regardless of whether or not they drove by Time 2 (n = 471). The results showed that there were significant differences in responses between the two time periods. Attitudes to speeding (that is, reckless/cautious, bad/good), DAQ attitudes (attitudes to speeding, not wearing seat belts and drink-driving all being acceptable) and intentions to speed mean scores dropped significantly from Time 1 to Time 2 (P < 0.001). Changes in responses over the two time periods for the speeding attitude items (unsafe/safe and un-enjoyable/enjoyable) were however not significant. The second paired samples test looked at the difference between responses made at Time 1 and Time 2 according to whether or not participants drove by Time 2. The results showed that whilst mean responses for both drivers (n = 207) and non-drivers (n = 262) dropped significantly from Time 1 to Time 2, scores on attitude to speeding being un-enjoyable/enjoyable increased significantly for both groups.
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An independent sample t-test run on each of the eight questions at Time 2 comparing responses of driving and non-driving participants did not reveal any significant differences. Discussion The results from this study have highlighted young driver sex differences, attitude changes and links between their pre-driver attitudes and behaviours with their current driving behaviour. The research aims have thus been met and have successfully identified the need for the implementation of pre-driver interventions that create safe attitudes to both driving and general road safety. The results have confirmed that young male drivers are higher sensation seekers than female drivers and seek out intense experiences, which could explain why young males are particularly over-represented in road accidents. In view of the fact that both Novelty and Intensity in sensation seeking were shown to correlate with engagement in driving violations, and that studies have reported that violations are associated with increased crash involvement, perhaps interventions need to be designed to target high sensation seekers in particular. Attitudes and speeding intentions for all participants decreased from Time 1 to Time 2, indicating that there were no significant differences between drivers and non-drivers responses in the second questionnaire. However, although both attitudes and intention to speed decreased and became more desirable over the year period, their attitude towards speeding being enjoyable increased. These results were surprising as it was anticipated that attitudes and intentions would increase with age and driving exposure, as both driver and passenger. Speeding intention scores at both sampling points were correlated with driving violations, errors and sensation seeking. Therefore, although intention scores decreased over the course of the study, participants with the highest scores at Time 1 and Time 2 were those most likely to engage in violations and errors whilst driving. This link between pre-driver speeding intention and future engagement in driving violations again highlights the need for intentions to engage in safe driving practices to be ingrained in adolescents before they learn to drive. Pre-driver attitudes and behaviours were predictive of driving behaviour. Attitudes towards speeding, drink-driving and not wearing seat belts were correlated with unsafe road crossing, play and social activity and non-engagement in protective behaviour on the road. These results suggest that there are links between positive attitudes to driving violations and pre-driver road user behaviour. In the group who became drivers, their pre-driver attitudes towards speeding and drink-driving along with their road user behaviour, specifically unsafe road crossing behaviour, correlated with self-reported engagement in driving violations. These results were further supported by the outcomes of several regressions, which revealed that the best predictors of future engagement in driving violations were pre-driver unsafe road crossing and play and social activity on the roads. Driving lapses and errors were best predicted by engagement in play and social activity on the road as predrivers and the gender of the driver. Females, for example, reported significantly
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more errors and lapses whilst driving. Interventions thus need to not only aim at changes in pre-driver attitudes but also at pre-driver behaviour. High-risk adolescent road users could thus be targeted through road safety campaigns. This study has thus provided evidence to support Jessor’s Problem Behaviour Theory, in that one form of problem behaviour, namely risky adolescent road user behaviour, was associated with another form of problem behaviour in the form of risky driving behaviour. The results also lend support to West et al.’s (1998) study, as those participants scoring high on Sensation Seeking reported riskier behaviour on the roads. Negative attitudes towards road safety were also linked to risk taking, thus supporting Iversen (2004), Ulleberg and Rundmo (2003) and Wundersitz and Burns (2005) studies. There were however a few limitations with this study. Firstly, it is not known whether or not road safety interventions were implemented in the schools that took part during the year period which could have helped towards lowering attitudes between the time points. Secondly, as the questionnaires were asking about socially undesirable behaviour it is entirely possible that participants completed the questionnaire in a more socially desirable manner or under-reported driving violations, errors and lapses. In future replications of this study intention to drink and drive and to not wear seat belts could be included to find out whether or not they are significant predictors of driving violations, errors or lapses. Also, it may be better to leave more than a year between sampling points, or to add a Time 3, to allow for drivers to gain more experience in an attempt to ascertain how their attitudes may or may not have changed. References Arnett, J. (1994). ‘Sensation seeking: a new conceptualization and a new scale.’ Personality and Individual Differences, 16 (2): 289–96. Elliott, M.A. and Baughan, C.J. (2003). The Behaviour of Adolescent Road Users. Department of Transport, Behavioural research in road safety, 13th Seminar, September 2003. Elliott, M.A. and Baughan, C.J. (2004). ‘Developing a self-report method for investigating adolescent road user behaviour.’ Transportation Research Part F, 7: 373–93. Iversen, H. (2004). ‘Risk-taking attitudes and risky driving behaviour.’ Transportation Research Part F, 7 (3): 135–50. Jessor, R., Turbin, M.S. and Costa, F.M. (1997). ‘Predicting developmental change in risky driving: the transmission to young adulthood.’ Applied Developmental Science, 1 (1): 4–16. Parker, D., Manstead, A.S.R. and Stradling, S.G. (1995). ‘Extending the theory of planned behaviour: the role of personal norm.’ British Journal of Social Psychology, 34: 127–37. Parker, D., Stradling, S. and Senior, V. (1998). The Development of Remedial Strategies for Driving Violations. TRL report 300. Ulleberg, P. and Rundmo, T. (2003). ‘Personality, attitudes and risk perception as
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predictors of risky driving behaviour among young drivers.’ Safety Science, 41: 427–43. West, R., Train, H., Junger, M., Pickering, A., Taylor, E. and West, A. (1998). Childhood Accidents and their Relationship with Problem Behaviour. Road Safety Research Report no. 7, Department for Environment, transport regions, London. WHO (2004). World Report on Road Traffic Injury Prevention: Summary. World Health Organisation: Geneva (http://www.who.int/violence_injury_prevention/publications/road_traffic/world_ report/summary_en_rev.pdf) WHO (2007). Youth and Road Safety. World Health Organisation: Geneva (http:// whqlibdoc.who.int/publications/2007/9241595116_eng.pdf). Wundersitz, L. and Burns, N. (2005). ‘Identifying young driver subtypes: relationship to risky driving and crash involvement.’ In L. Dorn (ed.), Driver Behaviour and Training, Volume II. Ashgate Publishing: Aldershot.
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Chapter 7
Prediction of Problem Driving Risk in Novice Drivers in Ontario: Part II Outcome at Two Years Laurence Jerome and Al Segal University of Western Ontario, Canada Introduction There are six million drivers in Ontario, 1.3 million or 22 per cent of whom are deemed to be problem drivers who have had previous driving offences or crashes and are at increased risk for ‘problem driving’ (Ministry of Transport of Ontario, 1991). One specific target group known to be at increased risk are younger, new drivers. New drivers are three times more likely to be killed than the average driver. They make up 15 per cent of licensed drivers and have 30 per cent of driver fatalities. In 2003, 1678 youths aged 15–24 years died as a result of injury, which represents 73 per cent of all deaths in this age group, one death in Canada every five hours. Motor vehicle crashes account for the majority (about 60 per cent) of unintentional injuries. Even more disturbing, for every youth who dies from trauma, more than ten have severe injuries often requiring one or more surgeries, prolonged hospital stays and rehabilitation. The consequences of severe trauma, especially to the brain, are often so devastating and permanent that prevention is a far better investment than late interventions and supportive care (Statistics Canada, 2003; Canadian Institute for Health Information, 2005). This is not just a problem in Canada: motor vehicle collisions continue to be the leading cause of death for 16–20-year-olds in the United States, Australia, New Zealand and most Western European countries (American Academy of Paediatrics Committees on Injury, 2006). A recent editorial opines, ‘We all appear to have become acclimatised to this public health epidemic. If 32 youths in Canada were dying each week from heart disease, influenza or meningitis, a huge outcry to stop this epidemic would be heard’ (Canadian Medical Association Journal, 2007). Statistics from ‘Drinking and Driving in Ontario Statistical Yearbook 1990’ indicates that the 19–24 year old groups are over-represented both in terms of nondrinking accidents as well as drinking related accidents. The search for ‘the holy grail’ of predicting which novice drivers will fall into the high-risk group remains elusive. Over a decade ago the then current best estimates were that two-thirds of all accidents were not predictable on the basis of current knowledge of driver characteristics and training (Ministry of Transport of Ontario, 1994). This statistic reflects the current state of knowledge.
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This study reports on a two year follow up of a group of novice drivers attending a high school driving programme who were screened at base line with on-the-road observation by experienced driving instructors who also completed a screening instrument based on the human factor literature thought to predict non-accidental traffic injury and shown in part one of this study to correlate with the current gold standard of risk prediction. The outcome at two years was measured by official driving records listing moving violations and collisions. The findings in this study will argue that human factors measured prior to obtaining a driver’s licence will have significant power to predict risk. Human factor research Gerald Wilde, writing in 1994 in his book Target Risk, found no statistical support that personal characteristics correlated with general accident risk. Although acknowledging some correlations with individual characteristics and collisions, he found the evidence that there is a powerful effect within specific individuals unconvincing. Whilst acknowledging demographic predictors such as age and gender, he emphasised the utility of changing the level of risk tolerance of the population as a whole as having more utility in accident prevention than concentrating on human factor research (Wilde, Robertson and Pless, 2002). However there is an extensive literature on high risk driving populations. A recent report on contributing factors to collisions stated that human factors, as opposed to vehicle and environmental factors are the predominant contributor to collisions (United States General Accounting Office [GAO], 2003). Risky driving behaviours are found to predict collisions and moving violations (Blows, Ameratunga, Ivers, Lo and Norton, 2005; McKnight and McKnight, 2000). Such behaviours include speeding, following too close, driving under the influence of alcohol, cell phone use and not using seatbelts while driving. These behaviours cluster in young drivers and a propensity to risk-taking contributes to increased rates of unintentional injury beyond the risk due to inexperience alone (Jonah, 1986). Numerous factors including demographic variables, personality and cognitive abilities have been explored to further understand their contribution to risky driving and collisions. Young males (Turner and McClure, 2003; Williams and Shabanova, 2003) as well as older (> 65 years) drivers (Preusser, Williams, Ferguson, Ulmer and Weinstein, 1998; Williams and Shabanova, 2003; Zhang, Fraser, Lindsay, Clarke and Mao, 1998) have been consistently related to increased negative driving outcomes. The evidence shows that both educational attainment and occupational status are inversely related to motor vehicle driver collision and injury (Hasselberg and Laflamme, 2003; Murray, 1998). The concept of accident proneness as it relates to unintentional driving injury was first elucidated in the psychiatric literature by Tillman and Hobbes (1949), Professors of Psychiatry at the University of Western Ontario. They described a characterological style in a group of accidentprone drivers referred by the Ministry of Transportation for recurrent accidents. The authors coined the phrase, often repeated in this literature, ‘a man drives as he lives’. Many studies have since presented evidence for associations between personality traits including risk taking, sensation seeking, impulsivity, difficulty in dealing with
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tension and controlling anger, substance abuse, antisocial tendencies as well as nonconformity and risky driving behaviours or collision (Deffenbacher, Deffenbacher, Lynch and Richards, 2003; Jonah, 2001; Tsuang, 1985). Psychosocial models of high risk driving including descriptions of temporary states involving high stress (Lagarde, Chastang, Gueguen, Coeuret-Pellicer, Chiron and Lafont, 2004) and Problem Behaviour Theory emphasising lifestyle factors including low parental involvement and negative peer and parental influence (Shope, Waller, Raghunathan and Patil, 2001; Shope, Raghunathan and Patil, 2003) have been related to risky driving and problem driving events. Cognitive abilities likely play a significant role in driving risk. Inattention and distractibility, which are directly related to risky driving behaviour, are cognitive factors that have been found to account for one-fourth of collisions (Treat et al., 1977). Poor risk perception, as well as impaired capacity to deploy appropriate judgment and reasoning while driving, have also been found to play a role in risky behaviours and negative driving outcomes (McKnight and McKnight, 2000; Ryb, Dischinger, Kufera and Read, 2006). Deficits in these higher order cognitive factors of executive functioning are thought to underlie risky driving behaviours. Such deficits are more evident in young and older drivers and in various clinical populations, and likely contribute to the higher collision rates found in these categories of drivers (Jerome, Segal and Habinski 2006, Jerome and Segal, 2000; McKnight and McKnight, 1993; Treat et al., 1977). Normal maturational immaturities in areas of the brain underlying executive function, evident in younger ages (Blakemore and Choudhury, 2006), together with inexperience, likely contribute to increased driving risk. Method Participating sites and collaborators This study was conducted through the University of Western Ontario Department of Psychology. Dr A. Segal and Dr Laurence Jerome are Adjunct Professors at UWO in the departments of Psychology and Psychiatry. Experimental participants were recruited from students enrolled in courses offered through the Driver Training Centre at Thames Secondary School. Thames Secondary School is located in London, Ontario and is part of the Thames Valley District School Board. The participants volunteered for this study and were provided with $20 compensation. Duration of study Following the initial data collection, the participant new drivers in this study were followed for a period of two years following the initial assessment. Procedure The official MTO driving records for the same 66 participants from Jerome and Segal (2005) were used in this study. The official driving record contained a spreadsheet
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for each participant listing any known moving violations and collisions. Only convictions are recorded within the official record. Previously the driving instructors had evaluated the driving risk of each of the volunteers. The risk ratings were based on the driving instructors’ impression of the students’ observed driving ability. The instructors were asked to identify the students’ risk based upon their observational experience of the participants’ risk severity. The literature on evaluation of driving risk, suggested that individual driving instructors are reliable at predicting problem driving based on their observations of students driving. At the termination of the study at two years, all participants were to be contacted by telephone to conduct an interview designed to elicit details of moving violations, collisions and driving exposure. The Screening Instrument Computerised measures of inattention and impulsiveness The stop-signal paradigm test The stop-signal paradigm (Schachar et al., 1993) was used to assess inhibitory control; subjects were engaged in a choice reaction time (go) task and attempted to inhibit their responses to the go task when they heard a stop signal. Reaction times to the stop signal (SSRT) and to the go signal (GoRT) were used to examine inhibition and response execution respectively. The Conners’ continuous performance test (Conners, CPT- II) This computerised instrument is the most widely used commercially available test of the variables of attention and behavioural inhibition (Conners, 2000). The participant must continuously respond to non-targeted letter stimuli, but inhibit responding to infrequent visual targets. The CPT has normative data developed on both clinical and normal populations. Subjective measures of driving behaviour made by driving instructors Driving instructor risk rating (risk rating) This is a visual analogue scale completed by the driving instructor after five hours’ observation of the student’s driving. The driving instructor responded to the question ‘rate this student on your estimation of current safety, based on your observations of their current driving behaviour’. The instructor placed an X on a ten centimetre line; the further from the origin on the left, the higher the rating of risk. Driving instructor checklist Objective severity ratings of problem driving were obtained from a semi-structured behavioural observation instrument, with demonstrated reliability and validity used in an English driving study, the Driving Instructor Checklist (West and Hall, 1998). This checklist was modified for North American expression and left-hand driving. No changes to content were made. This paper and pencil instrument asked driving instructors to rate the participant
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on driving skill, safety and future ability and safety, that is, the sections included Current Risk, Safety Risk and Future Risk. Subjective self-report measures of driving behaviour and personality style Barkley adult attention scale This is an 18 item self-report questionnaire providing scores for inattention as well as hyperactivity and impulsivity. The scale items are derived from the Diagnostic and Statistical Manual of the American Psychiatric Association fourth edition with age-corrected norms (Barkley and Murphy, 1998). Jerome driving questionnaire (JDQ) This is a visual analogue scale consisting of 12 measures of the subjects’ impression of their current and future driving style over the next 12 months. The subject was asked to place an X on the line distant from the origin to indicate their subjective rating of risk. The scale provides a measure of emotional and cognitive factors thought to reflect underlying executive function as it relates to driving. Health and life style questionnaire (HLS) Standard information regarding health status, current medication usage and current recreational drug usage and accident history was collected. The temperament and character inventory (TCI) The TCI (Cloninger, 1996) was given to evaluate the temperamental profile of impulsivity (Novelty Seeking) within the context of a broader assessment of other temperament and character traits. The TCI is a widely reference research instrument which has been shown to evaluate the temperamental characteristic of impulsivity. The TCI is computerised and presents 240 descriptive statements to which the participant responds ‘true’ (‘this statement describes me’), or ‘false’ (indicating the statement is incorrect). Normative data is available and the test is self-scoring within the software programme. There are no offensive or sexually provocative statements in the TCI. Youth risk behaviour surveillance system (YRBS) This is a widely used epidemiological survey instrument with known validated characteristics used in numerous studies evaluating health risk problem behaviours in community youth samples (Youth Risk Survey, 2001). Demographic questionnaire In addition to the above measures, standard demographic information in relation to age, gender, height, weight, grade point average in school, family composition and family occupation was collected. Telephone interview questionnaire This was a 22-item telephone questionnaire modelled after a questionnaire used by Russell Barkley (2002). A research assistant attempted to contact participants by phone and arrange a suitable time to administer the questionnaire.
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Results Outcome data The human factors and self-report predictors reported in Jerome and Segal (2005) were analysed with respect to the prediction of problem driving events as recorded in the official Ministry of Transportation of Ontario driving record taken approximately two years after licensing. Of the original sample of novice drivers (n = 66), approximately 27 per cent of students (18) had at least one problem-driving event that included either a moving violation or collision. Twenty violations and 16 collisions were found in the official record. There were no mortalities in the sample. Four participants had multiple collisions. These four drivers were identified by the driving instructor’s Risk Rating to be falling within the moderate to highrisk categories. A single participant had two alcohol-related violations and received a driving instructor high-risk rating. Whilst these anecdotal observations are interesting, when the entire dataset was considered, all four types of the driving instructor’s risk ratings and one additional composite risk rating (Driving Instructor Checklist plus Total Score) was not statistically significantly associated with any measure of driving outcome. Furthermore, the driving instructor’s risk ratings whilst identifying low risk participants failed to distinguish moderate from high-risk participant outcome. A follow-up telephone survey at two years post-licensing gathered information about driving exposure, collisions and violations. Only 32 of the original 66 participants could be traced. Eight collisions and six violations were self-reported that did not appear on the official driving record. Again these additional problemdriving events were unrelated to the Driving Instructor’s Risk Rating. Table 7.1 Human factors predictors of problem driving events (a) Total driving incidents Predictors Gender JDQ – risk taking (b) JDQ – anger (b) JDQ – daydreaming TCI – sentimentality TCI – cooperation TCI – compassion Barkley – inattentive Barkley – total CPT – variability
0.384 0.403 0.326 0.325
Correlation p < 0.001 p < 0.001 p < 0.01 p < 0.01
–0.253 –0.293 –0.303 0.292 0.315 0.250
p < 0.04 p < 0.01 p < 0.01 p < 0.02 p < 0.01 p < 0.04
Prediction of Problem Driving Risk (b) Collisions Predictors JDQ – risk taking (a) JDQ – risk taking (b) JDQ – alertness (b) (c) Violations Predictors Gender Grade average JDQ – anger (b) JDQ – daydreaming JDQ – risk taking (b) TCI – disorderliness TCI – cooperation Barkley – inattentive Barkley – total
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Correlation 0.282p < 0.02 0.237 p < 0.05 0.279p < 0.03
0.380 –0.291 0.364 0.477 0.383 0.275 0.283 0.369 0.368
Correlation p < 0.002 p < 0.01 p < 0.004 p < 0.0001 p < 0.002 p < 0.02 p < 0.02 p < 0.005 p < 0.005
Prediction of driving outcome Table 7.1 (a), (b) and (c) show the statistically significant correlations of human factor predictors to driving outcome as recorded in the official driving record. Section (a) describes the results for the total problem driving incidents; sections (b) and (c) report on collisions and violations, respectively. Linear regression analysis was applied to the Total Driving Incidents data. The model incorporated all the identified human factor predictors, accounting for 32 per cent of the common variance (using the Adjusted R2). The linear regression model for collisions only identified the JDQ-risk taking variable, accounting for six per cent of the variance. The JDQ-daydreaming variable and male gender were related to violations, accounting for approximately 37 per cent of the common variation. Table 7.2 (a), (b) and (c) presents the statistically significant results for the selfreport predictors of problem-driving events. Section 7.2 (a) describes the results for the total problem driving incidents; sections (b) and (c) report on collisions and violations, respectively. Linear regression analysis was applied to the Total Driving Incidents data. The model involved only the YRBS-other drugs predictor, accounting for 36 per cent of the common variance. The linear regression model for collisions only identified the HLS-head injury predictor. This accounted for roughly 19 per cent of the variance. A combination of YRBS-other drugs and HLS-other drugs used predictors accounted for 62 per cent of the violations variance.
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Self-report predictors of problem driving events (a) Total Driving Incidents; (b) Collisions; (c) Violations
(a) Total Driving Incidents Predictors HLS – Substance use-concern YRBS – Personal safety YRBS – Marijuana use YRBS – Other drugs
Correlation 0.345 0.546 0.252 0.514
p < 0.005 p < 0.0001 p < 0.04 p < 0.0001
(b) Collisions Predictors HLS – Rx medication use HLS – Head injury HLS – Accidental poisoning YRBS – Personal safety
–0.285 0.253 0.411 0.311
Correlation p < 0.02 p < 0.04 p < 0.001 p < 0.01
0.340
Correlation p < 0.01
(c) Violations Predictors HLS – Other drug use HLS – Substance use- concern HLS – Criticism for drug use HLS – Guilt for drug use HLS – Morning alcohol use HLS – Fighting while intoxicated YRBS – Personal safety YRBS – Violence YRBS – Marijuana use YRBS – Other drugs YRBS – Physical conditioning
0.391 0.275 0.356 0.281
p < 0.001 p < 0.03 p < 0.004 p < 0.02
0.322 0.532 0.329 0.338 0.622
p < .008 p < .0001 p < .009 p < .006 p < .0001
–0.270
p < .03
Linear regression models of problem driving outcome As part of the initial stages of constructing a screening instrument for the prediction of problem driving, stepwise linear regression analysis was conducted on all statistically significant predictors of problem-driving events. These results are shown in Table 7.3 (a), (b) and (c). As shown in this table, the Total Driving Incidents regression model involved a combination of human factor and self-report predictors accounting for 48 per cent of the variance. Similarly, a combination of human factor and self-report predictors yielded a model of collision prediction that accounted for 34 per cent of the common variance. Lastly, violations were only associated with two self-report measures of ‘other drug use’ that accounted for approximately 62 per cent of the variance.
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Table 7.3 Linear regression models of problem driving events (a) Total driving incidents Predictors YRBS – Other drugs, Gender, CPT – Variability
Adjusted R2 0.480
(b) Collisions Predictors YRBS – Personal safety, HLS – Head injury, HLS – Accidental poisoning, JDQ – Alertness (b), JDQ – Risk taking, JDQ – Risk taking (b)
Adjusted R2
0.340
(c) Violations Predictors YRBS – Other drugs, HLS – Other drug use
Adjusted R2 0.616
Discussion Limitations of this study include the small sample size. Whilst the period of exposure and the exposure dose (distance driven) was not measured directly, we feel confident that the relatively long period of observation and follow up combined with objective official MTO data should have captured most of the problem driving events in this group of novice drivers. Especially given current research suggesting the maximum period of collision and violation risk in novice drivers peaks within the first months of independent driving and declines rapidly after six months (McCArrt et al., 2003, Mayhew et al., 2003). Others have indicated that the official record prevalence figures are likely to be underestimations, excluding unreported minor collisions and moving violations that never came to police attention. The results of our telephone interview questionnaire demonstrates this. Whilst 50 per cent of participants’ self-reported data could not be traced at two years, the available data indicated moving violations and collisions not reported in the official record. The under-reporting may reflect a delay in data entry in the official record, failure to be convicted or minor collisions that were not reported where official reporting reflects more financially costly collisions. Our findings support those of Barkley (1993) and Nada Raja (1997), arguing that a combination of self-reporting and official driving records will likely produce the most comprehensive picture of driving outcome. The current prevalence of 27 per cent of our sample having problem driving is in keeping with the figures reported in Ontario of 25 per cent of random samples of drivers manifesting problem driving (Ministry of Transport of Ontario, 1991; 1994). Our study casts doubt on the utility of driving instructor ratings as the best measure of future risk (Donnelly et al., 1992; Dobbs, Heller et al., 1998; Dobbs and McCracken et al., 1998; West and Hall, 1998). This is the first study to report on the predictive validity of a driving instructor’s evaluation done at the time of the initial training two years after independent driving. To our knowledge there are no published studies
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evaluating the contribution of both ‘objective’ measures of ecologically valid realtime driving instructor evaluations at base line along with human factor measures of impulsivity and attention after two years of independent driving experience. Our data demonstrate that for novice drivers, the driving instructors’ future predictive accuracy was ‘no better than chance’. Previous authors have argued strongly against the predictive power of human factors in identifying at-risk groups of drivers (Wilde et al., 2002). Our data demonstrate that a combination of subjective self-report from novice drivers combined with human factors measures of attention and impulsiveness and temperament profiling predicts a significant percentage of the problem driving events. Interestingly, our self-report data on problem behaviour as assessed by the YRSB and HLS, unrelated to problem driving, did not support the notion of a more general factor of risky problem behaviour. The linear regression model of general problem behaviour excluded aggressive behaviours, accidental ingestion of poisonous substances, tobacco, alcohol and cannabis use, and sexual promiscuity as significant problem behaviour predictors of problem driving. This finding supports those of Beggs et al. (1999) who argued against a general factor of risk taking behaviour predicting problem driving. The exposure to ‘other’ recreational drugs was associated with moving violations but not collisions in this relatively young population. This may reflect a smaller, but more behaviourally deviant sub-group in our sample, where human factors are subordinate to specific problem behavioural patterns related to substance use. Collisions, an infrequent event compared with moving violations, are complex events likely influenced by a combination of human factors, deviant problem behaviour and the unpredictable nature of the road environment. Our findings support a model of problem driving based on human factors related to inattention and impulsiveness and a second more restricted model of lifestyle factors of deviant problem behaviour arguing for a transaction between predisposing and precipitating biological and environmental factors. All the human factor instruments used in this study are normally used to measure symptoms of inattention and impulsiveness, measures of executive function in clinical populations. The fact that clinical measures of executive function have such strong predictive power in normal populations is a unique finding in this field of study that has previously relied on instruments that assume human factors lie on a continuum. We would argue that indeed the utility of these clinical instruments measuring executive function in a normal population argues for the same continuum of severity of these human factors in both clinical and non-clinical populations. As we have previously argued deficits in executive function as it relates to problem driving can be seen as lying along a continuum across a range of categorical clinical diagnoses and ages ranging from ADHD, depressive illness and dementia (Jerome and Segal, 2000). Similarly, the findings in this study of deficits of impulsiveness and inattention might best be considered as continuous orthogonal variables that cut across the categorical distinction between normal and clinical populations. The findings of this study argue that the application of human factor research to predicting problem driving behaviours holds the promise of developing an instrument that could be both a sensitive and specific tool for detecting a high-risk population of young drivers. The negative findings regarding the predictive power
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of trained driving instructors, although counter-intuitive, is supported by other research findings that showed little benefit of current driving instruction techniques and later driving safety. (Mayhew et al., 1998). Our findings argue that given the underlying cognitive and temperamental vulnerabilities in these high-risk normal populations, the development of screening instruments to measure future risk of problem driving in normal populations should use measures of executive function as well as self-reported data. Such instruments may be used to guide cognitive behavioural interventions for at-risk individuals to improve their attention capacities and reduce impulsiveness. Indeed, this instrument may provide a metric for assessing improvement and predicting increased safety for driving. Such instruments could well incorporate driving simulators designed to measure the critical factors found to be predictive of problem driving. Perhaps a modified approach to driving instruction for these high-risk groups emphasising cognitive and behavioural measures of driving competence might incorporate the use of such instruments to assist driving instructors, as well as official examiners in deciding the readiness of novice drivers to graduate to the next level, that is, independent driving. References American Academy of Pediatrics Committees on Injury, Violence and Poison Prevention and on Adolescence. ‘The teen driver.’ Paediatrics 2006, 118: 2570– 81. Barkley, R.A. and Murphy, K. (1998). A Clinical Workbook (2nd ed.). New York: Guilford Press. Barkley, R.A., Guevramont, D.C., Anastropoulos, A.D., DePaul, G.J. and Shelton, T.L. (1993). ‘Driving-related risks and outcomes of attention deficit hyperactivity disorder in adolescents and young adults: a 3–5 year follow-up survey.’ Paediatrics, 92, 212–18. Barkley, R.A., Murphy, K.R., DuPaul, G.J. and Bush, T. (2002). ‘Driving in young adults with attention deficit hyperactivity disorder: knowledge, performance, adverse outcomes and the role of executive functioning.’ Journal of the International Neuropsychological Society, 8, 655–72. Begg, D.J., Langley, J.D. and Williams, S.M. (1999). ‘A longitudinal study of life style factors as predictors of injuries and crashes amongst young adults.’ Accident Analysis and Prevention, 31,1–11. Blakemore, S.J. and Choudhury, S. (2006). ‘Development of the adolescent brain: implications for executive function and social cognition.’ The Journal of Child Psychology and Psychiatry, 47(3–4), 296–312. Blows, S., Ameratunga, S., Ivers, R.Q., Lo, S.K. and Norton, R. (2005). ‘Risky driving habits and motor vehicle driver injury.’ Accident Analysis and Prevention, 37(4), 619–24. Canadian Institute for Health Information. (2007). ‘National trauma registry: 2005 injury hospitalizations highlights report.’ Ottawa: The Institute. Canadian Medical Association Journal, ‘What’s killing and maiming Canadian
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Youth?’ CMAJ March 13th 2007 176(6), 737. Centre for Disease Control and Prevention (2001). Youth Risk Behaviour Survey (2001), Atlanta. http://www.cdc.gov/nccdphp. Cloninger, C.R. (1996). ‘Assessment of the impulsive-compulsive spectrum of behaviour by the seven-factor model of temperament and character.’ In Oldham, J.M., Hollander, E. and Skodol, A.E. (eds). Impulsivity and Compulsivity. Washington, D.C.: American Psychiatric Press, 59–96. Conners, K.A. (2000). The New Conners’ Continuous Performance Test (CPT II) Computer Programme. Toronto, ON: MHS Inc. Deffenbacher, J.L., Deffenbacher, D.M., Lynch, R.S. and Richards T.L. (2003). ‘Anger, aggression and risky behaviour: a comparison of high and low anger drivers.’ Behaviour Research and Therapy, 41(6), 701–18. Deffenbacher, J.L., Huff, M.E., Lynch, R.S., Oetting E.R. and Salvatore, N.F. (2000). ‘Characteristics and treatment of high anger drivers.’ Journal of Counselling Psychology, 47(1), 5–17. Dobbs, A.R., Heller R.B. and Schopflocher, D. (1998). ‘A comparative approach to identify unsafe older drivers.’ Accident Analysis and Prevention. 30(3), 363–70. Dobbs A.R., McCracken, P.N., Carstensen, B.A., Kiss, I. and Triscott., J.A.C. (1998). ‘The evaluation of competence to drive.’ Paper presented at 1998 Canadian Consensus Conference on Dementia. Donnelly, R.E., Karlinsky, H.J., Young, M.L., Ridgley, J.N. and Lamble, R.W. (1992). ‘Fitness to drive in elderly individuals with progressive cognitive impairment.’ Ministry of Transport of Ontario. Hasselberg, M. and Laflamme, L. (2003). ‘Socioeconomic background and road traffic injuries: a study of young car drivers in Sweden.’ Traffic Injury Prevention, 4, 249–54. Jerome, L. and Segal, A.U. (2000). ‘ADHD, Executive Function and Problem Driving.’ The ADHD Report, 8(2): 7–11. Jerome, L. and Segal, A.U. (2005). ‘Prediction of driving accident risk in novice drivers in Ontario: the development of a screening instrument.’ 2nd International Conference on Driver Behaviour and Training, Edinburgh, Scotland. In Lisa Dorn (ed.), Driver Behaviour and Training, vol. 2, 207–22. Jerome L., Segal A. and Habinski L. (2006). ‘What we know about ADHD and driving risk: a literature review, meta-analysis and critique.’ Journal of the Canadian Academy of Child and Adolescent Psychiatry 15(3), August 2006. Jonah, B.A. (2001). ‘Sensation seeking, risky driving and behavioural adaptation.’ Accident Analysis and Prevention, 33, 679–84. Lagarde, E., Chastang, J.F., Gueguen, A. Coeuret-Pellicer, M., Chiron, M. and Lafont, S. (2004). ‘Emotional stress and traffic accidents: the impact of separation and divorce.’ Epidemiology, 15(6), 762– 6. Mayhew, D.R., Simpson, H.M. and Pak, A. (2003). ‘Changes in collision rates among novice drivers during the first months of driving.’ Accident Analysis and Prevention, 35(5), 683–91 Mayhew, D.R., Simpson, H.M., Williams, A.D. and Ferguson, S.A. (1998). ‘Effectiveness and the role of driver education and training in a graduated licensing
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system.’ Journal of Public Health Policy, vol. 19, no.1, 51–66. McCartt, A.T., Shabanova, V.I. and Leaf, W.A. (2003). ‘Driving experience, crashes and traffic citations of teenage beginning drivers.’ Accident Analysis and Prevention, 35, 311–20 McKnight, A.J. and McKnight, A.S. (1993). ‘The effect of cellular phone use upon driver attention.’ Accident and Analysis Prevention, 25, 259–65. McKnight, A.J. and McKnight, A.S. (2000). ‘The behavioural contributors to highway crashes of youthful drivers.’ Annual Proceedings – Association for the Advancement of Automotive Medicine, 44, 321–33. Ministry of Transportation of Ontario (1991). Accident Potential: An Ontario Drivers Record Study Summary Report. Ministry of Transportation of Ontario (1994). Ontario Road Safety Agenda. Murray, A. (1998). ‘The home and school background of young drivers involved in traffic accidents.’ Accident Analysis and Prevention, 30(2), 169–82. Nada-Raja,S., Langley, J.D., McGee, R., Williams, S.M., Begg, D.J. and Reeder, A.I. (1997). ‘Inattentive and hyperactive behavior and driving offences in adolescence.’ Journal of the American Academy of Child and Adolescent Psychiatry, 36(4), 515–22. Preusser, D.F., Williams, A.F., Ferguson, S.A., Ulmer, R.G. and Weinstein, H.B. (1998). ‘Fatal crash risk for older drivers at intersections.’ Accident Analysis and Prevention, 30(2), 151–9. Ryb, G.E., Dischinger, P.C., Kufera, J.A. and Read, K.M. (2006). ‘Risk perception and impulsivity: association with risky behaviours and substance abuse disorders.’ Accident Analysis and Prevention, 38, 567–73. Schachar, R. J., Tannock, R., Logan, G. (1993). ‘Inhibitory control, impulsiveness and attention deficit hyperactivity disorder.’ Clinical Psychology Review, vol. 13, 721–39. Shope, J.T., Raghunathan, T.E. and Patil, S.M. (2003). ‘Examining trajectories of adolescent risk factors as predictors of subsequent high-risk driving behaviour.’ Journal of Adolescent Health, 32, 214–24. Shope, J.T., Waller, P.F., Raghunathan, T.E. and Patil, S.M. (2001). ‘Adolescent antecedents of high-risk driving behaviour into young adulthood: substance use and parental influences.’ Accident Analysis and Prevention, 33, 649–58. Statistics Canada. Causes of Death, 2003. Cat. no. 84-208-XIE. www.statcan.ca/ bsolc/english/bsolc?catno=84-208-X. Tillman, W.A. and Hobbs, G.E. (1949). ‘The accident-prone automobile driver.’ The American Journal of Psychiatry, 106, 321–31. Treat, J.R., McDonald, N.S., Shinar, D., Hume, R.D., Mayer, R.E., Stansifer, R.L., et al. (1977). Tri-Level Study of the Causes of Traffic Accidents, Vol. I: Causal Factor Tabulations and Assessment. (Publication Number DOT-HS-805-085). Washington, DC: U.S. Department of Transportation. Tsuang, M.T. (1985). ‘Psychiatric aspects of traffic accidents‘ The American Journal of Psychiatry, 142(5), 538–46. Turner, D. and McClure, R. (2003). ‘Age and gender differences in risk taking behaviours as an explanation for high incidence of motor vehicle crashes in young
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males.’ Injury Control and Safety Promotion, 10(3), 123–30. United States General Accounting Office. (2003). Research Continues on a Variety of Factors that Contribute to Motor Vehicle Crashes. Washington DC: United States General Accounting Office. West, R. and Hall, J. (1998). Accident Liability of Novice Drivers, TRL Report 295. Crowthorne: Transport Research Laboratory. Wilde, G.J.S., Robertson, L.S. and Pless, I.B. (2002). ‘For and against: does risk homeostasis theory have implications for road safety?’ BMJ 2002, 324, 1149–52 Williams, A.F. and Shabanova, V.I. (2003). ‘Responsibility of drivers, by age and gender, for motor-vehicle crash deaths.’ J Safety Res, 34950, 527–31. Zhang, J., Fraser S., Lindsay, J., Clarke, K. and Mao, Y. (1998). ‘Age-specific patterns of factors related to fatal motor vehicle traffic crashes: focus on young and elderly drivers.’ Public Health, 112, 289–95.
PART 2 Emotions and Driver Behaviour
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Chapter 8
A Review of Studies on Emotions and Road User Behaviour 1
Jolieke Mesken,1 Marjan Hagenzieker2 and Talib Rothengatter3 DHV Environment and Transportation, Amersfoort, The Netherlands 2 SWOV Institute for Road Safety Research, Leidschendam, The Netherlands 3 University of Groningen, The Netherlands
Introduction In recent years, numerous studies have indicated the relevance of emotions for road user behaviour. Most of these studies focused on anger, although other emotions were sometimes also investigated. The studies show varying results, mostly due to inconsistencies in the use of concepts, methods and theoretical frameworks. In the present paper, a review is given of empirical studies on emotions and road user behaviour. First, studies that used emotion as an independent variable or a mediator will be reviewed. Second, studies focussing on the causes of emotions in traffic will be discussed. Third, the studies will be compared with regard to methodology, concepts and definitions. Finally, the conclusions of the review will be presented. The articles discussed in this review were collected using the following databases: Library SWOV Institute for Road Safety Research, PsychLit and Online Contents from Dutch Public Library Network. The set was extended with relevant titles from the reference lists. Articles included in the set contained one of the following search terms in relation to traffic (including traffic, driving, road and car): Affect, Emotion, Mood, Anger, Fear and Depression. Articles that were related to driver aggression alone were left out. This was done because some studies focus on aggressive behaviour only, without making a reference to any affective concept. Examples are the series of horn-honking studies (Doob and Gross, 1968; Deaux, 1971; Ellison, Govern, Petri and Figler, 1995) and studies on the prevalence of driver aggression (Joint, 1995). Furthermore, studies on stress and workload were excluded, again as far as no reference was made to an affective concept. The concept of stress refers mainly to a mismatch between task demands and personal capabilities (Lazarus and Folkman, 1984). Negative affect may be a result, but this is not always the case. Therefore, only those studies that explicitly mention this negative affect are included. Finally, studies on depression after being involved in an accident (Post Traumatic Stress Disorder) were left out because they do not attempt to explain driving behaviour.
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Studies on affect as independent variable or mediator: emotion and mood effects In this section, studies that used emotion or mood as a factor influencing driving behaviour are discussed. Some of these studies have used the affective concept for building a model (for example on aggressive driving) and thus the concept is used as a mediator. The studies took different aspects of the driving task into consideration. These aspects can be categorised into effects on task performance, effects on errors and violations, effects on aggressive and risky driving and effects on general road safety and accident involvement. Task performance Several studies investigated the relationship between mood states and task performance. As early as 1967, Heimstra, Ellingstad and De Kock measured drivingrelated performance in relation to mood. A simulated driving task was constructed, which consisted of a steering wheel connected to a control element, and a pedal. Performance measures included vigilance, reaction time, tracking performance, and speed maintenance. Mood was measured prior to the task by having subjects fill out a questionnaire (Mood Adjective Checklist; see Nowlis 1965). Four subscales were distinguished: anxiety, aggression, fatigue and concentration. No statistically significant correlations were found between mood scales and performance measures. When comparing those who scored high and low on aggression, anxiety and fatigue, subjects seemed to perform worse on the driving subtasks, although the authors did not report effect sizes and p-levels. Groeger (1996) studied the effect of mood on self-rated and instructor-rated driving performance. Mood was measured before and after a driving test, by MAACL (Multiple Affect Adjective Checklist), including subscales of hostility, anxiety and depression. Both the subject and the instructor made judgements of the subject’s performance at various moments during the drive, compared to a novice driver. The subject’s judgement was related to the extent to which their mood, as measured by the MAACL, changed during the drive. That is, if subjects felt more anxious, depressed and hostile after the drive than before, they judged their performance as worse. The instructor’s judgement was only related to the change in hostility in the subject: if subjects felt more hostile after the drive, their performance was rated as worse, but this was not the case for anxiety and depression. These results suggest that all mood states influence self-evaluation, but only hostility is related to actual driving performance. A follow-up study by Stephens and Groeger (2006) investigated whether emotions affect driving behaviour in a simulator. Participants (n = 24) performed a test drive in a driving simulator. During the drive, they encountered various traffic events, designed to interrupt their journey. Throughout the drive, participants were asked to give ratings of their emotions. Three emotions were considered: frustration, calmness and anger. Results showed that when drivers had to reduce speed because of traffic events, they reported more anger and frustration and less calmness. Also, those who had become angry accelerated more after the impeding event.
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Garrity and Demick (2001) also measured driving-related performance in relation to mood. An experienced observer evaluated the driving behaviour of 163 subjects during a test drive. A number of driving behaviours were scored on an observation form, and factor analysis on these data revealed four factors: responsiveness, manoeuvring, observation and cautiousness. Mood was measured prior to the drive by using the POMS (Profile of Mood States; see Mc Nair, Lorr and Droppleman, 1992). The only factor that was related to mood was cautiousness: respondents scoring high on depression, anger and fatigue were less cautious, whereas respondents who scored high on vigour-activity were more cautious. The results of these studies suggest that anger/aggression is related to a decrease in task performance as rated by a driving instructor, to increased acceleration after an impeding event and to a less cautious driving style. The other mood states did not show consistent effects. Errors and violations Instead of focussing on general task performance, some studies took specific errors and violations into account. Two studies investigated the relation between mood and the amount of errors made in a driving course. Appel, Blomkvist, Persson and Sjöberg (1980) studied whether mood affects performance on a difficult driving task. They presented a mood measure (Sjöberg, Svensson and Persson, 1978) to 55 driving school students before and after skid training. Performance on the skid training was observed by trained instructors. Several erroneous actions were scored. Correlations between performance and mood measures showed that respondents who felt more unpleasant, tired, tense and uncertain made more errors in skid training on a slippery road. Ford and Alverson-Eiland (1991) also studied errors in relation to anxiety. The anxiety levels of 107 students (11 groups of 6–12 subjects) were measured before participating in a motorcycle rider’s course, by using the State Trait Anxiety Inventory (Spielberger, Gorsuch and Lushene, 1970). Performance on the driving course was measured by the amount of errors made on a skills test, as observed by an instructor. The correlation between level of anxiety before the test ride and the amount of errors made was not significant. Yet when correlations were calculated separately for each group, for some groups significant correlations were shown. The authors conclude that anxiety seems to be a moderately influential factor in predicting the performance of the subjects. Stradling and Parker (1997) and Lawton, Parker, Manstead and Stradling (1997) studied the role of affect in predicting traffic violations. Earlier studies (Parker, Manstead, Stradling and Reason, 1992; Parker, Reason, Manstead and Stradling, 1995) had shown that attitudes, subjective norm and perceived behavioural control together affect the intention to commit violations. Lawton et al. (1997) investigated whether the inclusion of affect in this model makes the prediction of intention to commit violations more accurate. A questionnaire with a list of 12 traffic violations was presented to respondents. A factor analysis on the violation items revealed three factors (fast driving, maintaining progress and anger/hostility). Regression analysis showed that positive affective evaluations predicted all three types of violations.
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The study shows that anticipated affect might be a strong factor influencing traffic behaviour. These results also suggest that at least for a certain group of drivers, committing violations is associated with anticipated positive affect. However, Arnett, Offer and Fine (1997) did not find an association between positive affect and (speeding) violations. In this study, the role of several state and trait factors in driving behaviour was studied among 59 high school students who kept driving logs. Comparisons between indicated mood states were made regarding the extent to which the speed limit was exceeded. Anger was the only mood state for which a relation with driving speed was shown: respondents exceeded the speed limit to a greater degree when angry than when experiencing any other emotion. The relationship between anger and violations was also investigated by Lajunen, Parker and Stradling (1998). Their questionnaire study showed that respondents who rated their own safety orientation as high and their own perceptual-motor skills as low were less likely to report driving anger than those who rated their safety orientation as low and their perceptual motor skills as high. Also, driving anger was related to committing both Highway Code and aggressive violations. In a second study, Lajunen and Parker (2001) related driving anger to general (verbal and physical) aggressiveness on the one hand and to aggressive reactions on the other hand. Several path models were proposed, which suggested that the link between verbal aggressiveness and driving aggression was mediated by driving anger, whereas physical aggressiveness had a direct link to driver aggression. Parker, Lajunen and Summala (2002), in another questionnaire study, compared self-reported aggressive responses to driving anger by using samples from three European countries: Finland, UK and the Netherlands. Generally, those behaviours that provoke most anger also provoked the most extreme reactions. There were some differences between the countries in the amount of anger the different behaviours provoked. For example: in response to reckless driving, UK drivers reported more anger than Dutch or Finnish drivers. In sum, there is only limited evidence that a negative (tense, insecure) mood is related to errors. The results from studies connecting moods or emotions to violations are more consistent: anticipated positive affect is associated with violations, and there also seems to be a link between anger and violations. Especially the relation between anger and aggressive violations has received considerable attention. These studies are discussed separately in the next section. Aggressive and risky driving behaviour In traffic research on emotion, the anger-aggression relationship has been studied most extensively. The studies by Deffenbacher and colleagues used various scales (Driving Anger Scale [DAS], Driving Anger Expression Inventory [DAX], Drivers’ Angry Thoughts Questionnaire [DATQ]) to measure the effect on aggressive and risky driving. Aggressive behaviour was defined by Deffenbacher, Richards and Lynch (2004) as ‘behaviour based in anger and/or behaviour the goals of which are to harm, intimidate, threaten, dominate, retaliate upon, frustrate, or otherwise express displeasure with another driver or user of the roadway’ (p. 116). Risky behaviour is
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behaviour that ‘puts the individual and/or others at increased risk for injury, crash and/or property damage’ (Deffenbacher et al., 2004: p. 116). A range of questionnaire and driving log studies showed that driving anger is associated with self-reported aggressive and risky behaviour (Deffenbacher, Lynch, Oetting and Yingling, 2001), aggressive forms of anger expression (Deffenbacher, Lynch, Deffenbacher and Oetting, 2001; Deffenbacher, Lynch, Oetting and Swaim, 2002) and angry thoughts (Deffenbacher, Petrelli, Lynch, Oetting and Swaim, 2003). These studies consistently show a pattern of high anger drivers reporting more aggressive and more risky behaviours on the road, getting angry more frequently and more intensely and reporting more crash-related outcomes (for example, loss of vehicular control, close calls, minor accidents and so on). The conclusion that driving anger is related to a range of other anger-related and driving-related variables, as was drawn from the above reported studies, was based primarily on self-reports (questionnaires and driving logs). The study reported by Deffenbacher, Deffenbacher et al. (2003) is one of few studies which did not use only self-reports but also measures of simulated driving. Subjects were 121 first year students, either scoring high or low in driving anger. High anger drivers drove with a higher speed and shorter following distances than low anger drivers. In high impedance situations they were more likely to crash, and their state anger level increased more in high impedance situations than for low anger drivers. The authors conclude that drivers with a disposition to become angry behind the wheel are different from low driving anger drivers in terms of state anger, aggression, risky behaviours and negative driving outcomes. A second study, in which a simulator was used, was carried out by Ellison-Potter, Bell and Deffenbacher (2001). Also in this study, groups of high and low anger drivers were compared. Subjects were randomly assigned to experimental conditions: the respondents had to imagine either that they were anonymous or not, and respondents were exposed to either aggressive or neutral stimuli. They subsequently performed a test drive in a simulator, in which their aggressive driving behaviour (for example, speed, number of red lights run and collisions) was recorded. Results showed that respondents drove more aggressively when being anonymous and when exposed to aggressive stimuli. However, no main or interaction effects of driving anger were found on aggressive driving behaviour. So, whereas Deffenbacher in his studies consistently showed that high anger drivers had a propensity to report aggressive driving more frequently, Ellison-Potter et al. did not find such a difference. The authors suggest that this may be due to the fact that no actual provocation was involved in the task: subjects were not provocated by other road users. Instead, aggressive behaviour was operationalised by average speed, number of red lights run, number of collisions and number of pedestrians hit. In fact, Deffenbacher and colleagues did not measure actual aggressive behaviour either, but used self-report measures. In two studies, Knee and Neighbors (2001) and Neighbors, Vietor and Knee (2002) also studied driving anger and aggression using questionnaires. They used selfdetermination theory to explain aggressive driving. According to self-determination theory, people may have different tendencies to regulate behaviour. They may either have a more controlled orientation (their behaviour is regulated by contingencies
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and pressures) or a more autonomous orientation (their behaviour is regulated by interest and choice). In the first study (Knee et al., 2001), it was shown that people who have a more controlled orientation are more likely to behave aggressively on the road, and this link is mediated by driving anger. In the second study (Neighbors et al., 2002), in which driving logs were used, an extra component was added: not only trait motivation is important, but also how this trait affects the motivation in a specific situation (state or situational motivation). The results of the study showed that respondents high in controlled motivation report to experience more pressure and ego-defensiveness in driving situations, leading to more anger and aggression. Whereas in the previous studies the emphasis was on personal characteristics and their relation to driver aggression, Yagil (2001) focussed on cognitive processes. She proposed that drivers’ aggressive behaviour is determined by the type of attributions they make after frustrating behaviour by other drivers. Results of a questionnaire study showed that aggressive reactions were not affected by hostile attributions about the behaviour of the driver. Rather, it was general irritability and competitiveness that predicted aggression. The studies discussed in this section provide substantial support for the hypothesis that trait driving anger is related to a range of other traffic related variables such as states of anger during driving and aggressive or risky driving behaviour. Besides aggressive driving behaviour, trait anger was shown to be related to Highway Code violations. Other personal characteristics and their relation to anger and aggression were discussed in this section: drivers’ tendency to regulate behaviour is related to aggression, as is drivers’ attribution of responsibility. General road safety and (near) accidents Emotions thus seem to affect a range of safety-related behaviours. Some studies also investigated whether there also exists a connection with actual road safety. Underwood, Chapman, Wright and Crundall (1999) studied both causal factors and effects of anger on self-reported driving behaviour and self-reported involvement in a (near) accident. Respondents filled in questionnaires concerning driving anger (DAS, Deffenbacher, Oetting and Lynch, 1994), driving behaviour (DBQ, Reason, Manstead, Stradling, Baxter and Campbell, 1990) and social deviance (Social Motivation Scale, West, Elander and French, 1993). Then they kept driving logs over a period of two weeks using portable micro cassette recorders. Along with other trip characteristics such as congestion, length of trip, near accidents and so on, information regarding felt anger during the trip was recorded (description of the event, intensity of anger, whether or not the anger affected driving). No straightforward relation between congestion and anger was found. Correlations were calculated between felt anger during the trip and involvement in a near-accident during the same trip. Significant correlations were shown, but a closer examination of the driving logs revealed that felt anger was often a result of the near accident. On those occasions where anger was not a result of near accidents, it appeared that the frequency of reporting anger was related to near accidents on other occasions. In this case, felt anger was related to trait driving anger and mild social deviance.
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The authors conclude that anger might be both a cause and result of near accidents, although causality remains questionable because of the correlational design. Levelt (2003b) also used driving logs to examine respondents’ own evaluation of the effects of their emotions on road safety. Causes of drivers’ emotions were equally often an event prior to the trip, an event during the trip or thoughts that occurred during the trip. Subjects rated the effects of positive emotions on average as positive for road safety, and negative emotions on average as negative for road safety. However, in some instances, fear was rated as positive for road safety. This was for example the case in situations where fast responses were needed. Bañuls, Carbonell Vaya, Casanoves and Chisvert (1996) and Carbonell Vaya, Bañuls, Chisvert, Monteagudo and Pastor (1997) developed a questionnaire to measure anxious responses to driving: the ISAT (Inventory of Situations provoking Anxiety in Traffic). Four subscales were identified: situations related to selfevaluation or external evaluation, situations related to criticism and aggression, situations related to impediments and traffic jams, and situations related to evaluation by the authorities. Bañuls et al. (1996) compared professional and novice drivers with regard to their responses on ISAT and their self-reported accident involvement. Results showed that for novice drivers, anxiety responses to those situations that involve some kind of evaluation of driving may be connected to increased accident risk, whereas for professional drivers the more risky situations are anxiety responses to those situations that involve delays or impediments. Carbonell et al. (1997) used only professional drivers in the sample and compared two different subgroups (taxi drivers and lorry drivers) with regard to their ISAT scores and accident involvement. For taxi drivers, anxiety responses to delays and obstacles were most clearly related to accident involvement. Also for lorry drivers, anxiety responses to impediments to completing the job and, to a lesser extent, anxiety about verbal aggression, were related to accidents. The results of these studies show that different subgroups of drivers show different relations between anxiety producing situations and accident involvement. General driving performance is thus negatively associated with negative emotions such as hostility and anxiety, although when people are asked about their own evaluation of the effects of emotions on safety, some negative emotions were evaluated as positive for traffic safety as well. Conclusion The studies reviewed in this section show that emotions and moods may affect driving-related performance in a number of ways. The clearest results were shown for feelings of anger and hostility. These emotions seem to affect general task performance, but are also related to the commission of violations and to aggressive and risky driving. Furthermore, some results suggest that anxiety and feelings of tenseness are related to errors. None of the studies investigated the effects of emotions on cognitive processes while driving. This issue will be discussed further later in this chapter.
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Studies on antecedents of emotions and moods while driving A number of studies focussed on emotion as a dependent variable. These studies investigated the causes or antecedents of emotions while driving. Three categories can be distinguished: studies on the personality – emotion relationship, studies on the relevance of situational characteristics in causing emotions and studies on the treatment of maladaptive emotions. Personal characteristics The relations between personal characteristics and emotion in traffic have been studied in terms of mood (Dorn and Matthews, 1995) and anger (Malta, Blanchard, Freidenberg, Galovski, Karl et al., 2001; Richards, Deffenbacher and Rosén, 2002). Dorn and Matthews (1995) compared two contrasting hypotheses on the mood and personality relation. One is that mood is affected by general personality traits, as measured in a study by Eysenck Personality Inventory (EPI; see Eysenck and Eysenck, 1964). The other is that mood is affected by more context specific traits, as measured in a study by the Driving Behaviour Inventory (DBI; see Gulian, Matthews, Glendon, Davis and Debney, 1989). In the study, subjects first completed these two scales, and gave ratings of driving risk and competence. Then they were asked to perform either a passive or active driving simulator task. Afterwards, post-drive mood was measured by Uwist Mood Adjective Checklist (UMACL) which has three dimensions: Tension, Energy and Hedonic Tone. Post-drive mood was predicted better by DBI than by EPI. The subscale Dislike of Driving was the strongest predictor: subjects scoring high on this factor had higher ratings on postdrive tension and lower ratings on hedonic tone and energy. Negative post-task mood was related to negative appraisals of competence. Therefore, people high on Dislike of Driving have a disposition to make negative appraisals of their personal competence. This might cause them to make negative post-task appraisals of their performance, which leads to a negative mood. Malta et al. (2001) compared 14 aggressive and 14 non-aggressive drivers on their physiological response patterns and driving anger. The study showed that aggressive drivers, compared to controls, showed different physiological response patterns. Also, they had significantly higher scores on driving anger and anger expression. No differences were reported between aggressive and non-aggressive drivers on state anger. Richards et al. (2002) also compared two groups on their level of driving anger: 21 first year students scoring high and 38 first year students scoring low on ADHD (Attention Deficiency Hyperactivity Disorder). Respondents filled in various scales related to driving anger (DAS, DAXI) and kept driving logs for a period of three days. Differences between the two groups were found: high ADHD subjects reported higher scores on different anger-related measures like driving anger, anger expression, aggressive driving behaviour and risky driving behaviour. Taylor and Deane (2000) and Taylor, Deane and Podd (2000) compared drivingfearful subjects (selected by advertisements) who either had or had not been involved in a motor vehicle accident. Driving fear was measured by DSQ (Driving Situations Questionnaire, Ehlers, Hofmann, Herda and Roth, 1994). The two groups did not
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differ on severity of driving fear, or their general patterns of concern, although respondents that had been involved in an accident were more concerned about accident and injury than those that had not been involved in an accident. So, the two groups differ in the sense that for one group there is a clear object their fear is directed to, and the other group there is not. However this does not seem to affect fear intensity. Results thus show that several personal characteristics such as trait anger, dispositional aggression, ADHD and type of driving fear (which is in these studies treated as a personal characteristic or phobia) are related to emotions and moods in traffic, although no relations with state anger were shown. Situational characteristics Parkinson (2001) investigated anger frequency in driving and non-driving contexts. Results of a questionnaire study showed that driving situations involved a higher frequency of anger than non-driving situations. Lawton and Nutter (2002) also compared anger in a driving and non-driving context and found that subjects in their study are not more likely to get angry in traffic than non-traffic situations. However, Lawton and Nutter only once introduced traffic scenarios and asked about level of anger, whereas Parkinson asked how many times per month people got angry. Lawton and Nutter also reported a difference in the expression of anger, which is more frequent in driving than in non-driving situations. Both studies acknowledge that there are specific characteristics of the driving context, such as anonymity and lack of possibilities to communicate, which makes it different from other situations. Also, Chapman, Evans, Crundall and Underwood (2000) showed that the likelihood to act on anger depends on the context. A number of 211 drivers filled in questionnaires regarding anger in driving and non-driving contexts. Also, personal interviews were conducted. Results showed that in driving situations people report equal levels of felt anger as in non-driving situations, but in driving situations it is more likely that people react on their anger with aggression than it is in non-driving situations. So, there do seem to be differences in the nature of driving and non-driving contexts. Concepts and methods Definition of affective concept A range of different affective concepts have been used in the literature: mood, anxiety, anger, emotion, road rage, affective evaluations, fear, stress, affect and depression. Still, only five studies provide a definition of the concept. Ford and Alverson-Eiland (1991) studied the effects of anxiety on performance on a motorcycle riders’ course. They referred to Spielberger et al. (1970), defining anxiety as ‘subjective feelings of tension, apprehension, nervousness and worry, as well as activation or arousal of the autonomic system’. Deffenbacher et al. (1994) refer to another study by Spielberger et al. (1983) and define driving anger as a context-specific measure of trait anger.
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Trait anger is defined as a disposition to experience anger frequently, but still the concept of anger remains undefined. Joint (1995) studied road rage, and defined road rage as ‘more extreme acts of aggression, such as physical assault, that occur as a result of a disagreement between drivers’. In this case the concept is defined in terms of behaviour and not in terms of affect. Stradling and Parker (1997) and Lawton et al. (1997) defined affect in relation to intention to commit violations. In these studies, affect is defined as extra motive, outside the more rational motives such as personal norm and behaviour intention: ‘feelings […] that an individual expects to experience while performing a particular behaviour’. Parkinson (2001) used appraisal theory (Smith and Lazarus, 1993) and defined anger as appraisals of other-blame. The conclusion is that most studies do not offer a definition of the concepts they use. The definitions that are provided in the studies mentioned above are often not adequate. They sometimes refer to another undefined affective concept (as in trait anger), or their definition does not seem to cover the meaning of the concept (as in road rage, which implies more than just behaviour, as mentioned in the definition). Use of a theoretical framework Most of the articles reviewed do not use a theoretical framework to build and test hypotheses about the relations between emotions and driver behaviour. However, there are a few exceptions; mainly regarding aggressive driving. Knee et al. (2001) and Neighbors et al. (2002) try to explain aggressive driving by using self determination theory (Deci and Ryan, 1985). They found that a controlled motivational orientation was related to driving anger, which in turn was related to driver aggression. Other studies have applied both the frustration-aggression hypothesis (Lajunen and Parker, 2001) and social information processing theory (Yagil, 2001). Appraisal theories have been used in studies on both stress (Matthews et al. 1996) and emotion (Levelt, 2001; Parkinson, 2001). Since the appraisal theory of emotion evolved from transactional models of stress, this is not surprising. Some of the studies refer to a theoretical framework or concept, but do not say anything about affect-driving links. Some refer only to the affective concept, for example in articles on driving related fear (Taylor and Deane, 1999). They use the theory of Rachman; ‘three pathways theory of fear acquisition’. Also, in the extensive work on driving anger that was done by Deffenbacher and colleagues since 1994, a reference is made to the concept of state-trait anger (Spielberger, 1983) but no predictions are made on the relation between anger and driving based on this theoretical concept. Dorn and Matthews (1995) refer to the temperamental approach to personality (Tellegen, 1985) and to the transactional model of driving stress (Gulian et al., 1989) and thus make contrasting hypotheses concerning the relationship between personality and mood in a driving context. However, again no predictions are made on the mood-driving relationship. Stradling and Parker (1997) used the theory of planned behaviour (Ajzen, 1985). The theory is an attitude theory, which in itself does not refer to the emotion-driving relationship, but the theory was extended with affective evaluations to be more accurate in predicting the intention to commit violations.
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Measures Especially when studying the effects of emotions on driving behaviour, it would be best to have a real experiment: a design with a manipulation, a control group and random assignment to groups. However, none of the articles reviewed have actually manipulated anything, but measured the concept before and/or after a driving task. This measurement is also very diverse: about 20 different scales to measure affect have been identified. Some studies used existing scales, like (variations on) the Mood Adjective Checklist (Heimstra et al., 1967; Dorn and Matthews, 1995; Groeger, 1997); others developed scales for the purpose of the studies. One study (Malta et al., 2001) compared (self declared) aggressive drivers with non-aggressive drivers and used not only self-report scales (such as Driver’s Stress Profile, see Larson 1996; Driving Anger Scale, see Deffenbacher et al., 1994) but also physiological measures such as heart rate and blood pressure. Driving related performance was in most studies measured by self-report, such as questionnaires (for example, Banũls et al., 1996; Lajunen et al., 1998) or driving logs (for example, Arnett et al., 1997; Chapman et al., 2000; Richards et al., 2002). In some studies a driving simulator was used (Deffenbacher et al., 2003; EllisonPotter et al., 2001) although the type of simulator varied as well, for example in the older study by Heimstra et al. (1967), the simulator was just a steering wheel and a tracking device. Dorn and Matthews (1995) also used a simulator but used it only to be able to measure post-task mood; no driving parameters were collected. Three studies measured actual driving performance by observation, two of which were done in the context of driver training (Appel et al., 1980; Ford and Alverson-Eiland, 1991). One study (Garrity and Demick, 2001) used driving instructors to evaluate drivers who already possessed a driving licence. Conclusion The studies that used emotion as an independent variable showed a rather inconclusive picture. Anger and hostility influenced task performance as rated by a driving instructor, but other mood states did not show such an effect. Some relations were shown between emotions or moods on the one hand and errors or violations on the other. Negative mood was related to the amount of errors made during a skid course. Another study showed, however, no straightforward results between mood and errors. Violations were shown to be related to both positive and negative affect. Drivers were shown to report more violations when they expected to experience positive affect, but also when they scored high on trait anger. These studies consistently showed a relation between anger and aggressive and risky driving behaviour. Some explanations were provided: aggressive drivers have a different attribution style and different tendencies to regulate behaviour than drivers who are not aggressive. Studies on emotion and general road safety and (near) accidents showed relations between anger and anxiety on the one hand, and self-reported involvement in a (near) accident on the other. Furthermore, when subjects were asked to evaluate the impact
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of emotions on their own driving behaviour, they indicated that negative emotions were negative for road safety. From the studies that used emotion as a dependent variable, it can be concluded that certain personality factors such as general aggressiveness, ADHD, motivational orientation and safety orientation, are related to driving anger. However, driving anger always refers to a trait measure, which is not always made explicit in the text of the article. Although trait driving anger is related to state driving anger, no direct relations between other personality characteristics and state anger in traffic have been shown. As far as situational predictors of emotion are concerned, it is not clear whether anger in traffic is more frequent than anger in other situations. The expression of anger, however, is more likely in traffic than in non-traffic situations (Lawton and Nutter, 2002). The studies that have been discussed vary on a number of characteristics. The affective concept that is used is different in many studies. Emotion, mood, affect and personality are often used without specifying which concept is used, and why. Anger, for example, is sometimes used as a mood, sometimes as an emotion and sometimes as a trait. The use of a theoretical framework varies as well: some studies do not use such a framework, others do. The measures that are used are in most studies selfreport measures, although some studies mention the use of a driving simulator or an instrumented car. A difficulty with questionnaire studies is the issue of causality. Do angry drivers take more risk, or is a certain type of driver more likely to be involved in risky situations, which might elicit anger? In the Arnett et al. (1997) study, which showed a relation between anger and exceeding the speed limit, directionality is an issue. Mood, as well as speed, was recorded after the trip. The conclusion was drawn that respondents drove faster when angry than when in another emotion; however, the conclusion might as well have been: respondents were more angry when they were speeding, than when they were not speeding. The study by Underwood et al. (1999) also shows that the direction (in this case between anger and near accidents) might be both ways. Most studies that use the Driving Anger Scale did not find strong relations between state anger and aggression, suggesting that the link between driving anger and risky driving that are reported in these studies is more related to personality than to affect. Thus, whereas studies on determinants of emotions provided useful results, the effects of emotions in traffic are still unclear. Future research on the effects of emotion on driving behaviour should pay special attention to the use of a theoretical framework, a good definition of the concept and to the direction of causality, preferably by using an experimental approach. References Ajzen, I. (1985). ‘From intentions to actions: a theory of planned behaviour.’ In J. Kuhl and J. Beckmann (eds). Action Control: From Cognition to Behaviour. Berlin, Springer. Appel, C., Blomkvist, A., Persson, L. and Sjöberg, L. (1980). ‘Mood and achievement
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R-2003-8. Leidschendam, SWOV Institute for Road Safety Research. Malta, L.S., Blanchard, E.B., Freidenberg, B.M., Galovski, T.E., Karl, A. and Holzapfel, S.R. (2001). ‘Psychophysiological reactivity of aggressive drivers: an exploratory study.’ Applied Psychophysiology and Biofeedback, 26, 95–116. Matthews, G., Desmond, P.A., Joyner, L., Carcary, B. and Gilliland, K.A. (1996). ‘A comprehensive questionnaire measure of driver stress and affect.’ In T. Rothengatter and E. Carbonell Vaya (eds). Traffic and Transport Psychology: Theory and Application. Amsterdam, Pergamon. McNair, D., Lorr, M. and Droppleman, L. (1992). Profile of mood states (3rd ed.). San Diego, Educational and Industrial Testing Service. Neighbors, C., Vietor, N.A. and Knee, C.R. (2002). ‘A motivational model of driving anger and aggression.’ Personality and Social Psychology Bulletin, 28, 324–35. Nowlis, V. (1965). ‘Research with the mood adjective checklist.’ In: S.S. Tomkins and C.E. Izard (eds). Affect, Cognition and Personality. New York, Springer. Parker, D., Lajunen, T. and Summala, H. (2002). ‘Anger and aggression in three European countries.’ Accident Analysis and Prevention, 34, 229–35. Parker, D., Manstead, A.S.R., Stradling, S.G. and Reason, J.T. (1992). ‘Determinants of intention to commit driving violations.’ Accident Analysis and Prevention, 24, 117–31. Parker, D., Reason, J.T., Manstead, A.S.R. and Stradling, S.G. (1995). ‘Driving errors, driving violations and accident involvement.’ Ergonomics, 38, 1036–48. Parkinson, B. (2001). ‘Anger on and off the road.’ British Journal of Psychology, 92, 507–26. Reason, J.T., Manstead, A.S.R., Stradling, S.G., Baxter, J.S. and Campbell, K. (1990). ‘Errors and violations on the road: a real distinction?’ Ergonomics, 33, 1315–22. Richards, T., Deffenbacher, J.L. and Rosén, L.A. (2002). ‘Driving anger and other driving-related behaviours in high and low ADHD symptom college students.’ Journal of Attentional Disorders, 6, 25–38. Sjöberg, L., Svensson, E. and Persson, L.-O. (1978). ‘The measurement of mood.’ Scandinavian Journal of Psychology, 20, 1–18. Smith, C.A. and Lazarus, R.S. (1993). ‘Appraisal components, core relational themes and the emotions.’ Cognition and Emotion, 7, 233–69. Spielberger, C.D., Gorsuch, R.L. and Lushene, R.E. (1970). STAI, Manual for the State-Trait Anxiety Inventory (‘Self-Evaluation Questionnaire’). Palo Alto, California: Consulting Psychologists Press. Spielberger, C., Jacobs, G., Russel, S. and Crane, R. (1983). ‘Assessment of anger: the state-trait anger scale.’ In J. Butcher and C. Spielberger (eds). Advances in Personality Assessment. Hillsdale, NJ, Lawrence Erlbaum Associates, Inc. Stephens, A.N. and Groeger, J.A. (2006). Do Emotional Appraisals of Traffic Situations Influence Driver Behaviour? Paper presented at the Behavioural Studies Seminar, 3–4 April 2006, Bath, UK. Stradling, S.G. and Parker, D. (1997). ‘Extending the theory of planned behaviour: the role of personal norm, instrumental beliefs and affective beliefs in predicting driving violations’ In T. Rothengatter and E. Carbonell Vaya (eds). Traffic and
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Transport Psychology: Theory and Application. Oxford, Elsevier. Taylor, J.E. and Deane, F.P. (1999). ‘Acquisition and severity of driving-related fears.’ Behaviour Research and Therapy, 37, 435–49. Taylor, J.E. and Deane, F.P. (2000). ‘Comparison and characteristics of motor vehicle accident (MVA) and non-MVA driving fears.’ Journal of Anxiety Disorders, 14, 281–98. Taylor J.E., Deane, F.P. and Podd, J.V. (2000). ‘Determining the focus of driving fears.’ Journal of Anxiety Disorders, 14, 453–70. Tellegen, A. (1985). ‘Structures of mood and personality and their relevance to assessing anxiety, with an emphasis on self-report.’ In A.H. Tuma and J.D. Maser (eds). Anxiety and the Anxiety Disorders. Hillsdale, NJ, Erlbaum. Underwood, G., Chapman, P., Wright, S. and Crundall, D. (1999). ‘Anger while driving.’ Transportation Research Part F: Traffic Psychology and Behaviour, 2, 55–68. West, R.J., Elander, J. and French, D. (1993). ‘Mild social deviance, type-A behaviour pattern and decision-making style as predictors of self-reported driving style and traffic accident risk.’ British Journal of Psychology, 84, 207–19. Yagil, D. (2001). ‘Interpersonal antecedents of drivers’ aggression.’ Transportation Research, Part F: Traffic Psychology and Behaviour, 4, 119–31.
Chapter 9
A Comparison of the Propensity for Angry Driving Scale and the Short Driving Anger Scale Mark J.M. Sullman University of Hertfordshire, UK Introduction Driving evokes a wide range of emotions in people, including joy, frustration, anxiety, fear and anger. Anger is one of the emotions which has become increasingly researched over the last ten years. There are a number of reasons for this increase, including the fact that it is relatively common to experience this emotion while driving (Deffenbacher, Lynch and Oetting, 2002b). Furthermore, a number of studies have found that angry drivers engage more often in aggressive and dangerous driving behaviours (Dahlen, Martin, Ragan and Kuhlman, 2005; Deffenbacher, Oetting and Lynch, 1994). In fact Dahlen and Ragan (2004) went so far as to state that driving anger is one of the most influential predictors of aggressive and risky driving behaviour. Research has also found driving anger to be significantly related to near misses (Underwood, Chapman, Wright and Crundall, 1999) and crash related conditions, such as loss of concentration, losing control of their vehicle and crash involvement (Deffenbacher, Lynch, Oetting and Yingling, 2001; Deffenbacher, Deffenbacher, Lynch and Richards, 2003; Sullman, Gras, Cunill, Planes and FontMayolas, 2007). There are a number of ways in which driving anger can be measured, with two such scales being the Driving Anger Scale (DAS) and the Propensity for Angry Driving Scale (PADS). In addition, there are two versions of the DAS, a 14 item unidimensional measure and a 33 item multidimensional measure. The 14 item version of the DAS presents 14 different situations and asks the responding driver to report the degree of anger that each situation makes them feel. In contrast, the PADS presents a situation which is likely to evoke anger and then asks the respondent to indicate how they would respond by selecting one of four potential responses, which range from mild reactions (for example, slowing down) to more extreme (for example, ramming the other car). The DAS and PADS have both been found to have good psychometric properties. Research has shown the DAS to have good internal reliability, with alpha coefficients ranging from 0.80–0.92 (Deffenbacher et al., 1994; Deffenbacher, Filetti, Lynch, Dahlen and Oetting, 2002a). The alpha coefficients for the PADS have also been good, ranging from 0.85–0.89 (Dahlen and Ragan, 2004; Depasquale, Geller, Clarke and Littleton, 2001). Moreover, the test–retest reliability of both scales has also
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been shown to be high. The PADS has been found to have a four week test–retest reliability of 0.91 (DePasquale et al., 2001), while the DAS has been shown to have a ten week test–retest reliability of 0.84 (Deffenbacher et al, 2002a). As would be expected, both scales seem to have similar relationships with descriptive variables (for example, age and gender), as well as driving behaviours and crash related conditions. For example in the two studies which have used the original 19 item version of the PADS, neither reported any age differences (Dahlen and Ragan, 2004; DePasquale et al., 2001) and only DePasquale et al. (2001) reported a gender difference. Although some research has found females score more highly on the shortened version of the DAS (Dahlen and Ragan, 2004), most research has found no gender differences (for example, Dahlen et al., 2005; Deffenbacher et al., 1994). Also, in contrast to the research using the multidimensional version of the DAS (for example, Lajunen, Parker and Stradling, 1998; Sullman, 2006), no age differences were reported in the studies using the shortened version of the scale (Dahlen and Ragan, 2004; Dahlen et al., 2005; Deffenbacher et al., 1994). However, it should be noted that the studies using the short DAS have all used samples with very narrow age ranges, whereas the two studies (mentioned above) using the longer version of the scale used samples from the general population with much broader age ranges. The DAS has also been found to be related to aggressive and risky driving behaviour (Dahlen et al., 2005; Deffenbacher et al., 2001; 2002b) and other crash related conditions such as loss of concentration, loss of control and near misses (Dahlen et al., 2005; Deffenbacher et al., 2001). In addition, although one study found a relationship between the DAS and major accidents (Deffenbacher et al., 2002b), this has not been a common finding. In contrast, the one study which has related the PADS to crash involvement found that the PADS was correlated with both major and minor crashes (Dahlen and Ragan, 2004) and other crash-related conditions, such as loss of control and receiving tickets for violating road rules (Dahlen and Ragan, 2004). Furthermore, like the DAS, the PADS has also been found to be significantly related to aggressive and risky driving behaviour (Dahlen and Ragan, 2004). Although the PADS has been validated three times (DePasquale et al., 2001; Dahlen and Ragan, 2004; Maxwell, Grant and Lipkin, 2005), only two of these studies have used both the DAS and the PADS (Dahlen and Ragan, 2004 Maxwell et al., 2005). Furthermore, one of these two studies (Maxwell et al., 2005) modified the PADS by dropping four of the 19 items and also used a 21 item version of the DAS, rather than the 14 item version, meaning that the findings generated by that study were not comparable. Moreover, the only remaining study to compare the two scales relied solely upon psychology undergraduates as participants. This means that the participants were from a very restricted age range (medium 19) and were mainly female (75 per cent), calling into question the generalisability of these findings. This concern is highlighted further by the fact that in samples from the general population driving anger has been found to be related to both gender and age (Lajunen et al., 1998; Sullman, 2006). Therefore, it seems important that the PADS be investigated in a broader sample of drivers. The present study compared the PADS and DAS in
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order to test whether the previously found relationships could be generalised to a broader sample of drivers. Method Participants Participants were randomly selected from the electoral rolls in three New Zealand cities. These electoral rolls had recently been updated due to a general election. A random number generator was used to select page numbers and was also used to select five individuals from each selected page. The names and addresses of the individuals selected were copies from the electoral roll and they were then sent a package containing a cover letter, questionnaire and reply paid envelope. All participants were required to hold a valid New Zealand licence and to have driven at least once in the last six months. If the recipient of the pack did not meet these criteria, they were asked to pass the questionnaire on to a person in the household who did and whose birthday was the closest. In total, 600 questionnaires were posted out and 225 completed questionnaires were returned, giving a moderate response rate of 37.5 per cent. Questionnaire In addition to a number of descriptive variables (for example, age, gender, annual mileage and driving speed), the fourteen item Driving Anger Scale (DAS) (Deffenbacher et al., 1994) was used to measure driving anger. Participants were instructed to imagine each of the 33 situations happening to them and to rate the amount of anger evoked by each on a six point Likert scale, which ranged from 0 = ‘not at all’ to 5 = ‘very much’. The 19 item Propensity for Angry Driving Scale (PADS) was also included (DePasquale et al., 2001). The 19 items describe driving situations that are likely to evoke anger and participants were asked to indicate how they would respond to each situation by selecting one of four choices. These choices range from mild reactions (for example, slowing down) to more extreme reactions (for example, making obscene gestures). The answers were then scored according to the procedure outlined by Depasquale et al. (2001). The eight violation items from the Driving Behaviour Questionnaire (DBQ; Reason, Manstead, Stradling, Baxter and Campbell, 1990) were used to measure risky driving behaviour. The scale asked participants to report how frequently they engaged in eight different driving violations. This was reported on a six point scale, which ranged from 0 = ‘never’ to 5 = ‘all the time’. The ten item Trait Anger Scale was also used to measure general anger (Spielberger, 1999). The TAS measures how an individual generally responds when angry and is rated on a four point scale (1 = ‘almost never’, 4 = ‘almost always’). A number of crash related conditions were measured using Deffenbacher et al.’s (2000) Driving Survey, which included: moving violations (tickets), loss of
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concentration, loss of control, near misses (close calls), minor accidents and major accidents in the last three months. Finally, participants were also asked to report the speed they would normally travel at on five types of roads (motorway, highway, busy main street, winding country road and a residential road). The five items were standardised and combined to form a single speed item. Results The participants were aged 17 to 89 years old (M = 44.1, SD = 15.6) and they had on average 25.8 years driving experience. More than half were female at 56.7 per cent, with 43.3 per cent male. The vast majority had not been involved in a minor (91.9 per cent) or a major crash (99.1 per cent) during the last three months. The average annual mileage was 16 069 km/year and very few (4.1 per cent) had received a ticket in the three months prior to completing the survey. Factor analysis of the DAS produced one factor which accounted for 37.3 per cent of the variance and contained all 14 items. The factor analysis of the PADS also produced one factor, but this solution only accounted for 23.3 per cent of the variance and two of the items had loadings below 0.300. However, as removing either item failed to improve the alpha coefficient and to ensure comparability with previous findings, both items were retained. The PADS and DAS both showed good internal reliability, with 0.80 and 0.87, respectively. Good internal reliabilities were also found for the violations scale (0.73) and the Trait Anger Scale (0.82). As previous research has found gender differences on measures of driving anger (Dahlen and Ragan, 2004; DePasquale et al., 2001), the two driving anger scales were evaluated using a one way ANOVA. Surprisingly neither the PADS (F(1, 223) = 0.746, ns) nor the DAS (F(1, 223) = 0.492, ns) showed significant gender effects. Furthermore, there were no gender differences for minor accidents (F(1, 219) = 0.782, ns), major accidents (F(1, 218) = 1.543, ns), tickets (F(1, 219) = 1.751, ns), loss of concentration (F(1, 211) = 0.198, ns), loss of control (F(1, 216) = 0.023, ns), involvement in near misses (F(1, 216) = 1.972) or Trait Anger (F(1, 223) = 3.519, ns). There was, however, a significant difference for violations (F(1, 223) = 12.885, p < 0.001), with males reporting engaging more often in violations. (see Table 9.1) The PADS and DAS were correlated with a number of the background variables. Surprisingly, while the PADS was significantly correlated with age (–0.292, p < .001) and number of years (–0.282, p < 0.001) the DAS was not. The DAS and PADS were both positively related to self-reported driving speed (DAS .183, p < 0.01; PADS .277, p < 0.001), but neither was significantly related to annual mileage (0.124 and 0.026, respectively). The correlations between the main variables are presented in Table 9.2. The PADS was positively correlated with the DAS at 0.464, showing that although they are related they are certainly not measuring the same thing. The PADS and DAS were also both positively correlated with the TAS, violations and near misses. Interestingly the TAS was also correlated with both violations and loss of concentration.
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Table 9.1 Alpha coefficients, means and standard deviations by gender Measure
Alpha
PADS DAS Loss of concentration Loss of control Near misses Moving tickets Minor accidents Major accidents Violations TAS
0.80 0.87
0.73 0.82
Men M 39.76 37.43 1.85
Men SD 9.75 9.60 3.12
Women M 38.69 38.33 2.24
Women SD 8.74 9.35 8.03
0.19 1.17 0.10 0.13
0.68 2.62 0.57 0.41
0.18 0.77 0.03 0.08
0.56 1.52 0.18 0.33
0.00
0.00
0.02
0.13
2.15 16.14
0.57 3.29
1.88 17.14
0.52 4.37
PADS = Propensity for Angry Driving; DAS = Driving Anger Scale; TAS = Trait Anger Scale.
To further investigate the relationships between the driving anger scales and the two main variables they were correlated with (near misses and violations), four hierarchical multiple regressions were conducted. These were also to test whether the DAS and PADS were able to predict these two variables over and above that of the background variables and the TAS. Firstly, age, gender and annual mileage were entered in at Step 1, with the TAS being entered at Step 2. Finally the DAS and PADS were entered in at Step 3 in order to provide a direct comparison of the two. Table 9.3 shows that the descriptive variables and the TAS did not contribute significantly to the prediction of near misses, while the addition of the PADS and DAS resulted in a significant improvement in the prediction of near misses. Although it was only the DAS which was a significant predictor of near misses, when the same regression was performed without the DAS, the PADS made a significant contribution to the change in R2 (0.022, p < 0.05) and was a significant predictor of near misses (Beta 0.177, t = 2.135, p < 0.05). Table 9.4 shows that the descriptive variables (age, sex and annual mileage) were all significant contributors to the prediction of violations, as was the TAS. The addition of the PADS and DAS again resulted in a significant improvement in the prediction of violations, but this time it was the PADS which was the only significant predictor of violations. However, when the same regression was performed without the PADS the DAS made a significant contribution to the change in R2 (0.014, p < 0.05) and was a significant predictor of violations (Beta = 0.128, t = 1.999, p < 0.05).
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Table 9.2 Correlations between the main variables 1
2 – 0.464*** 0.532*** 0.361*** 0.018 0.122 0.179** •0.019 •0.113 0.006
3 • 0.393*** 0.213*** 0.046 •0.006 0.233*** •0.016 •0.046 0.119
• 0.318*** 0.144* 0.084 0.082 0.066 •0.039 0.055
4
5
• 0.203** 0.145* 0.283*** 0.092 •0.061 •0.053
6
• 0.251*** 0.201** 0.015 •0.037 •0.008
PADS = Propensity for Angry Driving; DAS = Driving Anger Scale; TAS = Trait Anger Scale * p < 0.05, ** p < 0.01, *** p < 0.001
7
• 0.105 •0.004 0.024 •0.029
8
• 0.016 •0.017 •0.044
9
• 0.482*** •0.014
• 0.239***
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1. PADS 2. DAS 3. TAS 4. Violations 5. Concentration 6. Lose control 7. Near miss 8. Tickets 9. Minor crash 10. Major crash
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Discussion This research found that the PADS and DAS were both significant predictors of violations over and above that of the descriptive variables and general trait anger. This is in support of previous research which has found both scales to be significant Table 9.3 Predicting near misses Step 1.
Variable Sex Age Mileage TAS PADS DAS
2. 3.
Beta •0.090 •0.054 •0.030 0.090 0.095 0.223
Change R2 0.010
R2 0.010
0.007 0.059**
0.017 0.077
t •1.270 •0.766 •0.419 1.203 1.098 2.867**
** p < 0.01
Table 9.4 Predicting violations Step 1.
2. 3.
Variable Sex Age Mileage TAS PONS DAS
Beta •0.173 •0.287 0.205 0.291 0.217 0.058
Change R2 0.177***
R2 0.177
0.073*** 0.043**
0.250 0.293
t •2.711** •4.572*** 3.206** 4.512*** 2.937** 0.864
** p < 0.01, *** p < 0.01
predictors of risky driving behaviour (Dahlen and Ragan, 2004; Dahlen et al., 2005; Deffenbacher et al., 2001; 2002b). However, it should be mentioned that when both driving anger scales were entered together it was only the PADS which was significant. This was probably due to the fact that the shared variance between the two scales was attributed to the stronger related of the two, the PADS. Also in agreement with previous research was the finding that the DAS was predictive of near misses (or close calls) (Dahlen et al., 2005). However, in contrast to the one previous study to investigate this (Dahlen and Ragan, 2004), the PADS correlated both with and without the DAS in the equation was predictive of near misses. This might be due to the differences in the demographic composition of the two samples. In contrast to previous research (Dahlen et al., 2005; Deffenbacher et al., 2001), the DAS was not found to be related to crash related conditions such as loss of concentration and loss of control, while the PADS was not related to involvement in either minor or major crashes. Perhaps one reason contributing to the reduced number of significant relationships was the relatively modest number of respondents. If the number of participants was slightly larger the number of significant correlations may
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also have been larger. For example, the PADS correlated at r = 0.122 with loss of control, which may have been significant with a larger sample size. Also along these lines the much lower number of factors related to the PADS and DAS may also be in part due to the differences in the demographic compositions of the respective studies. Most importantly the mean age here (44.1 years old) was much higher than the medium age of 19 years reported by Dahlen and Ragan (2004). As research has shown that younger drivers are more likely to engage in violations (Lajunen et al., 1998; Sullman, Meadows and Pajo, 2002), report higher levels of driving anger (Lajunen et al., 1998; Sullman, 2006) and are more frequently involved in crashes (for example, Sullman et al., 2002) it should not be surprising that more significant relationships were found with predominantly young participants. The level of anger or hostile reactions found here using the PADS (males = 39.8, females = 38.7) appeared to be considerably lower than that reported by DePasquale et al. (2001) (overall = 50) and Dahlen and Ragan (2004) (males = 47.2, females = 43.2). The level of driving anger reported on the DAS (males = 37.4, females = 38.3) also appeared to be lower than was reported by Dahlen and Ragan (2004) (males = 43.9, females= 47.2), Dahlen et al. (2005) (males = 45.6, females = 46.2) and Deffenbacher, White and Lynch (2004) (males = 46.9, females = 45.8). However, it should be noted that in all the previously mentioned studies the participants were university students, who on average were substantially younger than the sample in the present study. Therefore, it is not surprising that the present study would report the lowest levels of anger for both the PADS and DAS. This study provides further evidence that the DAS and PADS are measuring similar, but slightly different aspects of driving anger. The differences are evident firstly in the fact that they were moderately, but not strongly correlated (r = 0.464). This was also demonstrated by the fact that they had different relationships with the descriptive variables. Nevertheless, both scales were correlated with the same crash related variables, although the PADS was more strongly related to violations and the DAS more strongly related to near misses. Furthermore, both scales were predictive of near misses and violations. This study also found the PADS was more strongly correlated to the TAS than the DAS was. This was also mildly supported by Dahlen and Ragan (2004) and is probably due to the fact that what the PADS measures is more similar to the TAS than what the DAS measures. The DAS measures how strongly each of the fourteen situations causes the driver to feel angry, while both the PADS and TAS measure reactions to anger (for example, TAS – ‘hit someone who angered you’, PADS – ‘ram someone with your car’). One potential limitation of this study is the fact that the research relied solely upon self–reported data which is subject to self-report bias. However, as all participants were assured of anonymity and confidentiality there were no external pressures preventing them giving honest answers. Furthermore, while acknowledging that selfreport is not flawless, there is a substantial body of research which clearly supports the accuracy of data gathered in this manner (for example, Rolls, Hall, Ingham and McDonald, 1991; Walton, 1999; West, French, Kemp and Elander, 1993). The Driving Survey measures the accident related conditions over the previous three months. Although collecting the data over this period would help by reducing
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‘forgetting’, it also means very few individuals would have experienced minor or major accidents (8.1 per cent and 0.8 per cent, respectively), which are relatively rare events. Although previous research using this time period has found relationships between the PADS and accidents, it should be remembered that the previous research had a very young sample (medium 19) and young drivers are over-represented in accident statistics. Future research in this area should also collect the number of accidents over a longer period of time. In summary, although a number of the previously reported relationships between the driving anger scales and crash related conditions were replicated in this sample from the general population of drivers (rather than university students), many more were not. This research has also shown that although the PADS and DAS are measuring similar things, they are not exactly the same and should be thought of as complementary rather than competing scales. References Dahlen, E.R. and Ragan, K.M. (2004). ‘Validation of the Propensity for Angry Driving Scale.’ Journal of Safety Research, 35, 557–63. Deffenbacher, J.L., Filetti, L.B., Lynch, R.S., Dahlen, E.R. and Oetting, E.R. (2002a). ‘Cognitive-behaviour treatment of high anger drivers.’ Behaviour Research and Therapy, 40, 895–910. Deffenbacher, J.L., Huff, M.E., Lynch, R.S., Oetting, E.R. and Salvatore, N.F. (2000). ‘Characteristics and treatment of high-anger drivers.’ Journal of Counselling Psychology, 43, 131–48. Deffenbacher, J.L., Lynch, R.S., Oetting, E.R. and Swaim, R.C. (2002b). ‘The Driving Anger Expression Inventory: a measure of how people express their anger on the road.’ Behaviour Research and Therapy, 40, 717–37. Deffenbacher, J.L., Oetting, E.R. and Lynch, R.S. (1994). ‘Development of a driver anger scale.’ Psychological Reports, 74, 83–91. Deffenbacher, J.L., White, G.S. and Lynch, R.S. (2004). ‘Evaluation of two new scales assessing driving anger: the Driving Anger Expression Inventory and the Driver’s Angry Thoughts Questionnaire.’ Journal of Psychopathology and Behavioural Assessment, 26, 87–99. DePasquale, J.P., Geller, E.S., Clarke, S.W. and Littleton, L.C. (2001). ‘Measuring road rage: development of the Propensity for Angry Driving scale.’ Journal of Safety Research, 32, 1–16. Lajunen, T., Parker, D. and Stradling, S.G. (1998). ‘Dimensions of driver anger, aggressive and highway code violations and their mediation by safety orientation in UK drivers.’ Transportation Research Part F, 1, 107–21. Maxwell, J.P., Grant, S. and Lipkin, S. (2005). ‘Further validation of the propensity for angry driving scale in British drivers.’ Personality and Individual Differences, 38, 213–24. Parker, D., Lajunen, T. and Summala, H. (2002). ‘Anger and aggression among drivers in three European countries.’ Accident Analysis and Prevention, 34, 229–35. Reason, J., Manstead, A., Stradling, S., Baxter, J. and Campbell, K. (1990). ‘Errors
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and violations on the roads: a real distinction?’ Ergonomics, 33, 1315–32. Rolls, G.W.P., Hall, R.D., Ingham, R. and McDonald, M. (1991). Accident Risk and Behavioural Patterns of Younger Drivers. Southampton: AA Foundation for Road Safety Research. Spielberger, C.D. (1999). State-Trait Anger Expression Inventory (1st ed.). Odessa, FL: Psychological Assessment Resources. Sullman, M.J.M. (2006). ‘Driving anger amongst New Zealand drivers.’ Transportation Research Part F, 9, 173–84. Sullman, M.J.M., Gras, M.E., Cunill, M., Planes, M. and Font-Mayolas, S. (2007). ‘Driving anger in Spain.’ Personality and Individual Differences, 42, 701–13. Sullman, M.J.M., Meadows, M.L. and Pajo, K. (2002). ‘Aberrant driving behaviours amongst New Zealand truck drivers.’ Transportation Research Part F, 5, 217– 32. Underwood, G., Chapman, P., Wright, S. and Crundall, D. (1999). ‘Anger while driving.’ Transportation Research Part F, 2, 55–68. Walton, D. (1999). ‘Examining the self-enhancement bias: professional truck drivers’ perceptions of speed, safety, skill and consideration.’ Transportation Research Part F, 2, 91–113. West, R., French, D., Kemp, R. and Elander, J. (1993). ‘Direct observation of driving self-reports of driving behaviour and accident involvement.’ Ergonomics, 36, 557–67.
Chapter 10
Aggression and Non-aggression Amongst Six Types of Drivers1 Évelyne F. Vallières,1 Pierre McDuff,2 Robert J. Vallerand2 and Jacques Bergeron2 1 Télé-université, University of Québec at Montréal, Canada 2 Montréal University, Canada Introduction Aggressive driving can not only cause a lot of frustration among road users, but it can also be very dangerous, leading to violent incidents and driving accidents (Cook, Knight and Olson, 2005; Galovski and Blanchard, 2004; Galovski, Malta and Blanchard, 2006). It is a problem that road users come across frequently and, because of the diversity of aggressive driver types, it certainly represents a challenge for those concerned with road safety. Indeed, among aggressive drivers, there are those who are aggressive not only on the road but also in other aspects of their lives (Hennessy, 2005). Others, while not usually aggressive, admit to being sometimes aggressive while driving. In fact, whereas some drivers seem to always have an aggressive style of driving, others appear to become aggressive only in very specific situations or circumstances. Finally, there are those drivers who will rarely, if ever, be aggressive on the road, no matter what the situation or the circumstances they are facing. In sum, this seems to suggest that aggressive driving is multifaceted and that aggressive drivers are a heterogeneous group. In the last 30 years, aggressive drivers and their behaviours have been the focus of numerous studies. Research has shown that aggressive drivers are more often males than females, especially in cases of severe aggressive behaviours (Beck, Wang, Mitchell, 2006; Hennessy and Wiesenthal, 2001). It is also well established that aggressive drivers tend to be younger (for example, Shinar and Compton, 2004). Male drivers who hold vengeful and dangerous driving attitudes (Hennessy, 2005) and/or frequently commit driving violations (Maxwell, Grant, Lipkin, 2005), are also more aggressive on the road. Relative to situational variables, aggressive driving has been found to be more frequent at rush hours than at any other time of the day, and the presence of passengers has been associated with less aggressive driving (Shinar and Compton, 2004). Various variables related to personality and familial background have also been studied extensively. For example, in her recent studies, Malta and her 1 Authors’ note: This study was supported by Programme d’action concertée Fonds Nature et Technologies MTQ-SAAQ (project 2002-SR-86395). Québec, Canada.
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colleagues have found that numerous psychiatric and behavioural disorders, as well as familial antecedents such as oppositional defiant disorders, alcohol and substance abuse disorders, prevalence of self and familial anger problems and family conflicts, were associated with aggressive driving amongst young drivers (Malta, Blanchard and Freidenberg, 2005). Other dispositional measures, such as aggressiveness, narcissism or extraversion, have also been found to relate to aggressive driving (Britt and Garrity, 2006). Thus, the role of dispositional anger in fuelling aggressive behaviours has been established (Deffenbacher, Deffenbacher, Lynch and Richards, 2003; Deffenbacher, Richards and Lynch, 2004). Moreover, studies evaluating interventions aimed at reducing anger have confirmed the major role played by anger in aggressive driving (Galovski and Blanchard, 2004). However, even if the role of anger as a trigger to aggressive driving has been confirmed, few studies have looked at the psychological processes and cognitive variables leading to anger and aggressive driving reactions. Among the cognitive possible determinants of aggressive driving, perceived intent, which is usually implied in most definitions of aggressive driving, has rarely been studied. This could be due to the fact that driving behaviours such as risky driving are often considered aggressive, even though they may not have been performed intentionally (Maxwell et al., 2005). However, even if a risky driving manoeuvre is not done intentionally, attributional theory would predict that perceived intent of a negative encounter will lead to anger and, subsequently, to reactive aggression (Weiner, 1995; Weiner, Graham and Chandler, 1982). In other words, even if a frustrating manoeuvre was not done intentionally, the more intentional that frustrating manoeuvre will be perceived, the more angry the person subjected to it will feel and the higher the probability of an aggressive reaction. The link between perceived intent and reactive aggression has been supported in various contexts, in particular amongst aggressive children (see Crick and Dodge, 1994, for a review). These studies showed that in ambiguous situations aggressive children tend to infer greater intent and, consequently, to behave more aggressively than non-aggressive children (Dodge, 1980). This result suggests that perceived intent could be a significant determinant of driving anger and aggressive driving. In fact, a recent study by Vallières and her colleagues (Vallières, Bergeron and Vallerand, 2005) shows support for the link between perceived intentionality, anger and reported aggressive driving reaction. However, the comparison of perceived intent among aggressive and non-aggressive drivers was not investigated. For example, in a case where aggressive and non-aggressive drivers were confronted by the same frustrating driving manoeuvre, would aggressive drivers perceive more intent than the non-aggressive ones? And if they did, would the difference be significant only in situations that were ambiguous, such as in the Dodge study, or would it also occur in situations that were clearly intentional or clearly non-intentional? In other words, would it depend on the type of situation? Further, would aggressive drivers differ between themselves by the intensity of their reactions or by the characteristics of other drivers? The purpose of the present study was to address these questions. A first step was to establish a typology of drivers, ranging from the most aggressive to the least aggressive, using multivariate cluster analysis. A second step was to verify if some
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personal and driving characteristics, driving behaviours, attitudes and beliefs could differentiate between the different types of drivers. These analyses were based on three sets of variables in two types of situations. The three sets of variables were perceived intentionality, anger and aggressive driving reactions. These variables were measured in two types of hypothetical situations in which a frustrating driving manoeuvre by another driver was done intentionally or unintentionally. Method Participants A total of 480 college and university students took part in the study (mean age = 25.6 years old, SD: 7.13). The majority of the participants (n = 276) were males (mean age = 25.8 years old, SD: 7.09), 161 were females (mean age = 25.3 years old, SD: 7.20) and gender was unknown for 43 of them. All subjects were part of one of four studies conducted on various aspects of aggressive driving and the results of which are described elsewhere. Procedure and questionnaire The questionnaires were given in class with the approval of the professor or teacher. Only those participants who had a driving licence and who voluntarily agreed to participate were given a questionnaire to fill out. They were informed that the questionnaires were anonymous and were assured that their responses would be treated in a confidential manner. All the questionnaires of the studies reported here contained hypothetical scenarios describing situations that one might experience while driving. There were two types of situations describing the driving manoeuvre of another driver. In some of the scenarios, the action of the other driver was clearly intentional (intentional condition), whereas in others it was clearly unintentional (unintentional condition). Some of the participants answered the questions for the two types of situation and some others for only one type of situation. When a questionnaire included more than one scenario of each type (maximum of two scenarios by type), a total score was obtained for each type of scenario. The scenarios had been selected on the basis of the results of previous pilot studies. All participants (n = 480) answered questions relative to socio-demographic variables, driving record, style of driving and love of driving. All participants were also asked to read each scenario as if the situation were really happening to them personally and to indicate on the appropriate scales the level of intentionality they attributed to the other driver’s action (one item), how angry they would feel in such a situation (one item) and their behavioural reactions (depending on the questionnaire: five to eight items: alphas: 0.68 to 0.88). Answers were rated on five point Likert scales. Cluster analyses were based on these three sets of variables in each of the conditions (intentional and unintentional).
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Some participants (n = 217) answered the following sub-scales of the Driving Anger Expression Inventory (DAX) (Deffenbacher, Lynch, Oetting and Swain, 2002): verbal aggression (12 items; α = 0.86), physical aggression (11 items; α = 0.89) and car using (11 items; α = 0.86). These sub-scales assess how verbally and physically aggressive one is reacting when angered while driving, and whether one uses the car to express anger. The items of the three subscales are rated on a five point scale ranging from one (‘almost never’) to five (‘almost always’). The validity of the DAX and its internal reliability has been found to be adequate (Deffenbacher, Lynch, Deffenbacher and Oetting, 2001; Deffenbacher et al., 2002). Participants (n = 183) also answered measures inspired from the TPB (Theory of Planned Behaviour: see Ajzen and Fishbein, 1980; Ajzen, 1991). The TPB is a theoretical framework that has been used in numerous studies to understand the role of some cognitive and affective variables in changing various attitudes and behaviours such as attitudes toward speeding (Parker, 2002) and the commission of driving violations (Parker, Manstead, Stradling, Reason and Baxter, 1992). Prevention programmes built on its theoretical constructs have been successful in changing risky driving behaviours (for example, Stead, Tagg, MacKintosh and Eadie, 2005). The items assessed: 1. indirect attitudes, such as behavioural beliefs and outcomes evaluation (for example, ‘To put pressure on the other driver is extremely bad/good.’ Six items each, α : 0.63 and 0.76); 2. direct attitudes (for example, ‘In this situation, the fact of tailgating is totally aggressive/harmless.’ Four items, α: 0.81); 3. indirect subjective norms, such as normative beliefs and motivation to comply (for example, ‘Most of my friends would agree with the fact that I tailgate in this situation.’ Six items each, α: 0.75 and 0.87); 4. direct subjective norms (for example, ‘In this situation, most people that are important to me would totally agree/disagree with the fact that I tailgate.’ Three items, α: 0.67); 5. indirect perceived control, such as control beliefs and perceived power (for example, ‘Police cars around the area would prevent me from tailgating.’ Five items each, α: 0.56 and 0.60); 6. perceived behavioural control (for example, ‘For me, to tailgate the other car is extremely difficult/easy.’ Four items each, α: 0.76) ; and 7. behavioural intentions (for example, ‘In this situation I intend to tailgate the other car.’ Four items, α: 0.88). All measures were rated on scales ranging from –3 to +3 and were recoded on scales ranging from 1 to 7.
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Results Cluster profiles As discussed above, in order to identify possible subtypes of drivers, cluster analyses were conducted on the following three sets of variables (perceived intentionality, anger, behavioural reactions) in the two conditions (intentional, unintentional scenarios). Among the various cluster analysis strategies available, we combined Ward’s hierarchical clustering using squared Euclidean distances along a TwoStep clustering approach. Using SPSS, the TwoStep clustering approach allows the selection of the number of clusters according to a Bayesian Information criterion (BIC). Each of the variables included in the analysis were standardised in order to eliminate possible effects due to different variances. This approach suggested a six clusters solution. In addition to being theoretically meaningful, this solution was tested among selected subgroups of participants and was found sufficiently stable to be retained as the final solution. As 21 participants did not fit any of the six clusters, they were excluded from further analyses. As can be seen in Table 10.1, clusters one and two, which include 54 and 38 persons respectively, consist of those drivers who are the most aggressive in intentional as well as non-intentional situations. Together, these clusters represent 20 per cent of the sample, and as they consist of the most aggressive drivers we refer to them as the highly aggressive profile. The most numerous clusters, three and four, include 106 and 110 participants respectively and together they represent 47 per cent of the sample. Because their mean scores on behavioural aggressive reactions are in-between those of the other groups, they are called the mildly aggressive profile. There are 98 and 54 participants respectively classified in clusters five and six (33 per cent of the sample). The mean score of these drivers in both types of scenarios suggest that these drivers are rarely, if ever, aggressive and are qualified, for that reason, as the non-aggressive profile. A closer look at the two groups of the aggressive profile in Table 10.1 shows that they differ from each other by the intensity of anger and behavioural reaction they report, particularly in the intentional condition. As can be seen, group two reports more anger and more intense aggressive reactions than group one in this condition. In the unintentional condition, clusters one and two differ only by the level of intentionality perceived. Indeed, participants in cluster one perceive the other driver’s action as being intentional even though it is clearly unintentional. In the mildly aggressive profile, both groups of drivers (three and four) differ not only in the way they perceive the other driver’s intention, but also by the intensity of their affective and behavioural reactions. In the intentionality condition, group three participants react similarly to the aggressive profile in their way of perceiving intentionality and in their anger reaction. We find a somewhat inverse pattern with group four, as the participants from that cluster react similarly to the non-aggressive profile in the intentional condition. As for the non-aggressive profile, even though both groups show negative mean scores on all the variables in both conditions, these scores confirm that the group six participants are definitively the least aggressive.
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Table 10.1 Mean standardised scores on the scales defining the young driver clusters Clusters Scale
1. (n = 54)
Non-Intentional Scenario Perceived 0.82 intention Anger 0.81 Reaction 1.30 Demographics Age (years) 24.2 (5.2) Sex1 12.8 (% Male) Sex 8.9 (% Female)
3. (n = 106)
4. (n = 110)
5. (n = 98)
6. (n = 54)
0.34
0.45
-0.19
-0.17
-0.67
11.8**
0.38
1.25 1.93
0.75 0.29
-0.31 -0.42
-0.78 -0.51
-0.58 -0.75
72.1** 108.4**
0.71 0.77
0.10
0.08
0.34
-0.19
-1.47
43.3**
0.61
0.89 1.43
0.39 0.27
0.24 -0.06
-0.61 -0.73
-1.36 -1.04
64.7** 101.6**
0.69 0.77
25.3(7.5)
24.5(5.0)
25.4(7.6)
27.5(8.6)
26.6(7.3)
2.7*
0.03
7.8
21.6
23.8
23.4
10.6
8.9
25.0
25.6
18.5
13.1
Note: For each measure, higher scores indicate higher levels of the variable *p < 0.05, **p < 0.001 1 X2 = 3.9 ns
F
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Intentional Scenario Perceived 0.40 intention Anger 0.70 Reaction 0.84
2. (n = 38)
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Table 10.1 also presents the distribution of those subtypes according to two of the most used socio-demographic variables: age and gender. Analysis of variance on the mean age between our six groups reveals an overall age effect but no pairwise difference according to the Scheffe post hoc analysis. On the other hand, we found no difference according to gender, as a comparable percentage of males and females can be observed in each of the clusters. Self-reported aggression when angered In order to validate the nature of the six subtypes uncovered, we compared their mean scores on the three DAX subscales (see Table 10.2). The results obtained with the univariate analysis of variance showed important mean differences for the six groups on verbal and physical aggression and use of car. Scheffe post hoc analysis revealed that the two groups of the aggressive profile are significantly more aggressive than the other profiles on the three measures and that they do not differ between themselves on these same measures. Even though groups three and four of the mildly aggressive profile do not significantly differ from one another on those measures, both are significantly more verbally aggressive than participants from the non-aggressive profile. Table 10.2
Cluster DAX subscales: Variables Verbal aggression1 Physical aggression2 Car using aggression3
Mean of reported verbal aggression, physical aggression and car using aggression
Mean aggressive behaviour (SD) 1. n = 23 39.9 (6.6) 21.5 (9.8) 28.2 (9.8)
2. n = 17 39.1 (7.9) 19.5 (7.6) 29.0 (6.7)
3. n = 48 33.2 (7.4) 15.2 (5.1) 21.1 (7.0)
4. n = 58 30.5 (7.8) 14.3 (4.1) 17.0 (5.7)
5. n = 47 23.6 (7.1) 12.8 (3.8) 16.8 (6.1)
6. n = 24 25.8 (7.7) 12.5 (2.0) 13.6 (3.3)
F(5,214) 24.3 p = 0.01 12.0 p = 0.01 23.1 p = 0.01
η2 0.35 0.21 0.34
Note: Differences between groups are significant at p < 0.01. 1 Score: 12 to 60 2 Score: 11 to 55 3 Score: 11 to 55
Self-reported driving variables Next, the subgroups were compared in terms of their driving habits, speeding, number of speeding tickets, accident(s) and their love of driving. The results (Table 10.3) show a graduated decline in percentage of those reporting frequent speeding excess according to the subtypes. In fact, while 82 per cent of the aggressive drivers
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reported having often exceeded the speed limit, that percentage falls progressively to 53.8 per cent for the non-aggressive groups. Another significant difference between groups was found for speeding ticket violations, with group two participants reporting the highest percentage of speeding tickets relative to the others. Remember that this group is the one reporting the most aggressive reactions in both types of conditions. That group also reports the highest percentage of accidents and love of driving, but overall those differences between groups are not significant. Table 10.3
Cluster Variables
Percentage of high speed driving, accidents, speeding tickets, enjoyment of driving and the mean of reported verbal and physical aggression and car-use aggression
1. n = 51
2. n = 34
Driving behaviours (%) High 82.4% speed 74.3% driving1 One and + accidents2 25.5% 36.1% Speeding tickets3 Love driving4
3. n = 102
4. n = 110
5. n = 98
6. n = 52
X2
71.6%
70.9%
64.3%
53.8%
X2 = 11.8 *
19.6%
26.4%
23.5%
25%
X2 = 4.2, ns.
41.2%
50%
41.6%
33.0%
38.1%
19.2%
X2 = 11.8 *
62.7%
73%
66.3%
58.2%
61.9%
53.1%
X2 = 5.1, n.s.
1
% of those who like to speed often % of those who had had one or more accidents in the last three years 3 % of those who had one or more speeding tickets in the last three years 4 % of those who love to drive enormously Differences between groups on high speed driving and speeding tickets were significant at p < 0.05. In bold, adjusted standardised residual are greater than 2. 2
Attitudes, subjective norms, perceived control and behavioural intention Finally, the six groups were compared on ten subscales of the TPB (Table 10.4). Results of the analyses conducted on the direct and indirect variables related to the TPB revealed significant differences between clusters on four of the variables: outcome evaluation, motivation to comply, direct perceived control and intention to tailgate in the intentional condition. These differences mean that some of the drivers having an aggressive profile (group one) agree more than the other drivers with having behaviours such as putting pressure on another driver by tailgating in order to progress more rapidly in traffic. Some of these aggressive drivers (group two) also think that they have a high control over their driving, more so than the other drivers,
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and (in groups one and two) when they feel they are prevented from driving the way they wish to drive, they feel more justified to drive aggressively. Table 10.4
Attitudes, subjective norms, perceived control and intention to act aggressively in the intentional condition
Cluster 3. n= 53
4. n = 21
5. n= 40
ATTITUDES, M (SD) Indirect attitudes Behavioural 5.28 beliefs (0.73) Outcomes 4.57 evaluation (1.5)
5.00 (0.63) 4.33 (1.0)
5.20 (0.93) 4.26 (1.1)
5.00 (0.63) 3.67 (1.5)
5.42 (0.84) 3.79 (1.2)
5.18 (1.1) 3.10 (1.0)
3.32 (0.78)
3.38 (0.75)
3.04 (0.63)
2.58 (0.92)
3.05 (0.85)
2.66 (0.72)
SUBJECTIVE NORMS, M (SD) Indirect subjective norms Normative 3.85 3.33 beliefs (1.0) (1.2) Motivation to 4.36 4.83 comply beliefs (1.3) (1.5)
3.86 (0.97) 5.34 (1.1)
3.33 (0.82) 5.33 (0.52)
3.63 (0.95) 5.58 (0.84)
3.27 (0.65) 5.36 (0.67)
3.90 (0.84)
2.61 (1.3)
3.88 (1.2)
3.48 (0.77)
5.33 (0.82) 4.50 (0.55) 3.62 (1.2)
5.05 (0.91) 4.31 (0.82) 4.90 (0.98)
5.63 (1.0) 4.73 (0.65) 4.00 (1.5)
1.6 ns 0.9 ns 3.3 p< 0.01
3.21 (1.4)
4.63 (1.1)
3.52 (1.4)
7.2 p< 0.00
Direct attitudes
Direct subjective norms
1. n= 23
3.86 (1.01)
3.61 (0.90)
PERCEIVED CONTROL, M (SD) Indirect behavioural control Control beliefs 4.71 5.00 4.97 (0.99) (0.63) (0.75) Perceived power 4.14 4.17 4.20 (0.66) (0.75) (0.93) Direct 5.16 4.79 4.85 perceived (1.1) (0.84) (0.84) control BEHAVIOURAL INTENTION, M (SD) Intention to 5.38 5.46 4.70 tailgate (0.76) (0.87) (0.90) Range of scores: 0–7
6. n= 22
F ()
2. n= 24
Variables
0.4 ns 2.5 p< 0.05 1.7 ns
1.1 ns 2.7 p< 0.05 2.1 p= 0.08
η2
0.02 0.13
0.09
0.06 0.14
0.11
0.09 0.05 0.16
0.30
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Discussion The results presented here support a classification of drivers based on the three set of variables studied in the present research, namely perceived intentionality, reported anger and aggressive behaviours. In that sense, the present study is innovative. A classification is uncovered based on variables that parallel a motivational sequence and are process related (Vallières et al., 2005). Indeed, the typical cluster analyses are usually based on personal characteristics and personality variables which are less amenable to change than the process related variables used in the present study. The cluster analyses show that drivers can be differentiated along a continuum from high aggression to low aggression. Furthermore, the present results reveal that aggressive drivers are not a homogeneous group and that there is more than one type of aggressive drivers. The same can be said about the mildly aggressive and the nonaggressive groups, as neither appear to be homogeneous groups. The present results also reveal that the two most aggressive groups (aggressive profile) differ from each other by their cognitive interpretation of the situation and by the intensity of some of their reactions. For example, in the intentional condition, even though both groups interpret the situations in a similar way, the second group’s reactions are definitively more intense. However, in the non-intentional condition, the two groups interpret differently the situations. Indeed, if group two perceives correctly the low intentionality of the other driver’s action, group one perceives the depicted driver’s action as intentional even though it is clearly unintentional. Another surprising result relates to the comparable percentage of males and females in the different groups. That result disagrees with those found in other studies (for example, Beck, et al.,; Hennessy and Wiesenthal, 2001; Shinar and Compton, 2004) but is similar to some results obtained by Deffenbacher and his colleagues (Deffenbacher et al., 2003). These researchers suggest that there are probably more similarities than differences between male and female students in terms of aggressive driving reactions. Our results seem to agree with that statement. However, since our clusters are based on process variables (perceived intention, anger, reaction) we may also make the hypothesis that our results ensue from the type of variables used in the present cluster analyses. Our results could also mean that the actual process leading to driving aggressiveness is the same for both genders. Further studies should explore more thoroughly this hypothesis. The results presented here could be useful in planning better training for young drivers. In particular, in drawing their attention on how their perception of another driver’s action can have important impact on their own or on others affective and behavioural aggressive reactions, they may become more aware of the importance of not attributing intent to others’ driving actions thoughtlessly. In spite of the fact that the validity of the categories uncovered in the present study needs to be confirmed in other research, particularly with participants other than university or college students, the findings of the present study are of interest, notably in how they suggest that drivers vary greatly by their interpretation of other drivers’ actions and by the intensity of their reactions to these interpretations. As such, driver training providers might consider this aspect in training inexperienced drivers.
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References Azjen, I. (1991). ‘The theory of planned behaviour.’ Organizational Behaviour and Human Decision Processes, 50, 179–211. Azjen, I., Fishbein, M. (1980). Understanding Attitudes and Predicting Social Behaviour. Englewood Cliffs, NJ: Prentice Hall. Beck, K.H., Wang, M.Q. and Mitchell, M.M. (2006). ‘Concerns, dispositions and behaviours of aggressive drivers: what do self-identified aggressive drivers believe about traffic safety?’ Journal of Safety research, 37, 159–65. Britt, T.W. and Garrity, M.J. (2006). ‘Attributions and personality as predictors of the road rage response.’ British Journal of Social Psychology, 45, 127–47. Cook, L.J., Knight, S. and Olson, L.M. (2005). ‘A comparison of aggressive and DUI crashes.’ Journal of Safety Research – Traffic Records Forum proceedings, 36, 491–3. Crick, N.R. and Dodge, K.A. (1994). ‘A review and reformulation of social information-processing mechanisms in children’s social adjustment.’ Psychological Bulletin, 115, 74–110. Deffenbacher, J.L., Deffenbacher, D.M., Lynch, R.S. and Richards, T.L. (2003). ‘Anger, aggression and risky behaviour: a comparison of high and low anger drivers.’ Behaviour Research and Therapy, 41, 701–718. Deffenbacher, J.L., Lynch, R.S., Deffenbacher, D.M and Oetting, E.R.,(2001). ‘Further evidence of reliability and validity for the Driving Anger Expression Inventory.’ Psychological Reports, 89, 535–40. Deffenbacher, J.L., Lynch, R.S., Oetting, E.R. and Swain, R.C. (2002). ‘The Driving Anger Expression Inventory: a measure of how people express their anger on the road.’ Behaviour Research and Therapy, 40, 717–737. Deffenbacher, J.L.,Richards, T.l. and Lynch, R.S. (2004). ‘Anger, aggression, and risky behaviour in high anger drivers.’ In J.P. Morgan (ed.). Focus on Aggression Research. Hauppauge, NY, US: Nova Science Publishers, 115–156. Dodge, K.A. (1980). ‘Social cognition and children’s aggressive behaviour.’ Child Development, 51, 162–70. Galovski, T.A. and Blanchard, E.B. (2004). ‘Road rage: a domain for psychological intervention?’ Aggression and Violent Behaviour, 9, 105–127. Galovski, T.E., Malta, L.S. and Blanchard, E.B. (2006). ‘Aggressive driving: significance and scope of the problem.’ In Galovski, Tara E.; Malta, Loretta S.; Blanchard, Edward B. (eds). Road Rage: Assessment and Treatment of the Angry, Aggressive Driver, 3–14; Washington, DC, US: American Psychological Association, xi, 250. Hennessy, D. A. (2005). ‘Driving vengeance and wilful violations: clustering of problem driving attitudes‘ Journal of Applied Social Psychology, vol. 35(1), 61–79. Hennessy, D.A. and Wiesenthal, D.L. (2001). ‘Gender, driver aggression, and driver violence: an applied evaluation.’ Sex Roles, 44, 11/12, 661–76. Malta, L.S., Blanchard, E.B. and Freidenberg, B.M. (2005). ‘Psychiatric and behavioural problems in aggressive drivers.’ Behaviour Research and Therapy, Maxwell, J.P., Grant, S. and Lipkin, S. (2005). ‘Further validation of the propensity
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for angry driving scale in British drivers.’ Personality and Individual Differences, vol. 38(1), 213–224. Parker, D. (2002). ‘Changing drivers’s attitudes to speeding using the theory of planned behaviour.’ In Rutter, and Quine, L. (eds). Changing Health Behaviour. Buckingham: Open University Press, 138–52. Parker, D., Manstead, A.S.R., Stradling, S.G., Reason, J.T. and Baxter, J.S. (1992). ‘Intentions to commit driving violations: an application of the theory of planned behaviour.’ Journal of Applied Psychology, 77, 94–101. Stead, M., Tagg, S., MacKintosh, A. M. and Eadie, D. (2005). ‘Development and evaluation of a mass media theory of planned behaviour intervention to reduce speeding.’ Health Education Research, 20, 1, 36–50. Shinar, D. and Compton, R. (2004). ‘Aggressive driving: an observational study of drivers, vehicle, and situational variables.’ Accident Analysis and Prevention, 36, 429–37. Stradling, S.G. and Parker, D. (1997). ‘Extending the theory of planned behaviour: the role of personal norm, instrumental beliefs, and affective beliefs in predicting driving violations.’ In J.A. Rothengatter and E. Carbonell (eds). Traffic and Transport Psychology: Theory and Application, Amsterdam: Pergamon, 367–74. Vallières, E.F., Bergeron, J. and Vallerand, R.J., (2005). Weiner, B., (1995). Judgments of Responsibility: A Foundation for a Theory of Social Conduct, New York: Guilford. Weiner, B., Graham, S. and Chandler, C.C. (1982). ‘Pity, anger, and guilt: an attributional analysis.’ Personality and Social Psychology Bulletin, 8, 226–32.
Chapter 11
The Influence of Age Differences on Coping Style and Driver Behaviour Elizabeth Andrews and Stephen Westerman University of Leeds, UK Introduction There are established age differences in driver performance (Parker, McDonald, Rabbitt and Sutcliffe, 2000). In this research we are particularly concerned with the experiences and performance of older drivers. Relative to younger drivers, older drivers are more frequently involved in specific types of accidents, such as those involving multiple-vehicles and those at complex junctions (Hakamies-Blomqvist, 1994). In large measure, age differences in driving performance may be attributable to age-related changes in physical and cognitive abilities. Older adults report problems with visual and auditory functioning that impact driving (Kline and Scialfa, 1996) and are also likely to experience decline in cognitive abilities (although see Rabbitt, 1993), including speed of information processing (Salthouse, 1992) and memory (Craik and Jennings, 1992). Impaired cognitive function has also been related to poorer driving ability in older drivers (McKnight and McKnight, 1999). Studies of age-related differences in self-report measures of driving behaviour and driver stress have produced results that are only partially consistent with these patterns of age-related changes in ability and driving performance. When considering the typology of aberrant driving behaviours developed by Reason, Manstead, Stradling, Baxter and Campbell (1990) (that is, violations, errors and lapses), a number of studies report no age effects with regard to scores for more serious errors (for example, Aberg and Rimmo, 1998; Parker, Lajunen and Stradling, 1998; Westerman and Haigney, 2000). Violations (for example, running red lights) have been consistently found to be more often reported by younger drivers (Parker et al., 1998; Westerman and Haigney, 2000). Given the described age-related decline in physical and cognitive abilities, it might be anticipated that older drivers would experience greater driving-related stress. This would be consistent with current cognitive models of stress (for example, Lazarus and Folkman, 1984) in which appraisal of coping resources relative to task performance demands is a central determinant of the individual’s stress experience. However, it has been reported that older drivers experience relatively lower levels of general driver stress (Parker et al., 2001; Matthews, Dorn and Glendon, 1991). In part this may be attributable to reduced aggression in older drivers but greater associated concentration (Matthews, Desmond, Joyner, Carcary and Gilliland, 1997) – although this latter effect was very small in research by Westerman and Haigney (2000).
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There are a number of potential explanations for a degree of dissociation between driving performance and self-reports of driving behaviour/stress. These include the following, non-mutually exclusive, possibilities. First, it may be that there are age differences in willingness to report certain types of driving behaviour. Somewhat contrary to this, Lajunen and Summala (2003) reported little social desirability bias on self-reported measures of poor driving behaviours (for example, errors) in a public compared to a private setting, and drivers generally report the quality of their driving as being very good (Groeger and Grande, 1996). This could result in restricted range effects when using self-reporting. In this context, it might be argued that older drivers have unrealistic assessments of their abilities and do not take into account age-related changes. However, this runs counter to the findings of Parker, Macdonald, Sutcliffe and Rabbitt (2001). Nevertheless, it is possible that older drivers are more susceptible to memory deficits for driving performance. Related to this, one study reported that 14 per cent of people involved in injury-provoking accidents failed to recall the event a year later (Loftus, 1993) and memories for near-accidents are also forgotten fairly rapidly (Chapman and Underwood, 2000). The relatively lower stress levels self-reported by older drivers may result from the effects of additional driving experience (over years) that produces more advanced driving skills. Decreased mental workload and therefore an increased level of available attention resources is associated with greater driving experience (Patten, Kircher, Ostlund, Nilsson and Svenson, 2006). A related explanation of age-related performance maintenance is that through extended practice important components of the driving task are sufficiently automated to be protected from the effects of age-related cognitive decline (Hasher and Zacks, 1979). However, contrary to these suggestions, for some older drivers, reduced driving exposure (for example, low mileage) may equate to ‘inexperience’ and lower levels of driving ability. The final potential influencing factor that we consider here is that older drivers adopt compensatory strategies that protect their driving performance (McKnight and McKnight, 1999). This could happen at a ‘macro’ level, such that they are more selective of when they drive and consequently the driving situations they experience (for example, not driving on busy roads or at night) and this has a positive safety effect (Hakamies-Blomqvist, 1994). However, the evidence is rather mixed with regard to the extent that drivers with cognitive and visual impairments limit their driving exposure (Stutts, 1998). Compensatory processes might also operate at a ‘micro’ level, such that older drivers adapt their driving technique to match their physical and cognitive abilities. This would enable older drivers to achieve a better match between driving demands and resources available to meet those demands. Age-related changes in coping strategy (see Gulian et al., 1989b) would be consistent with age differences in sensory, physical and cognitive abilities (see Anstey Wood, Lord and Walker, 2005; Rabbitt, 1993) and personality (see Matthews et al., 1991) and may therefore be implicated in changes in driving stress and behaviour. The concept of coping plays a particularly important role in current conceptualisations of stress. Stress is thought to result when coping resources are perceived as insufficient to meet anticipated situational demands (Lazarus and Folkman, 1984). Ineffective coping strategies may lead to the experience of
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higher levels of driver stress (McCrae and Costa, 1986). This applies to drivingrelated stress (Gulian, Matthews, Glendon, Davies and Debney, 1989a). The effects of driver stress will be determined by drivers’ assessments of their availability of coping resources in relation to experienced driving demands (Matthews, Dorn and Hoyes, 1992). In this paper we examine the association between measures of driving behaviour, driver stress and driving performance, and consider the effects of age differences. In particular, we focus on the possibility that age-related differences in methods of coping impact assessments of driving behaviour, driving performance and also the experience of driving stress. Method Participants Forty-two younger drivers (22 females, 20 males) aged between 20 and 40 years (mean 31.0, SD 5.9 years) and 40 older drivers (22 females, 18 males) aged 60 years or above (mean 68.5, SD 6.3 years) were recruited from the Leeds Advanced Driving Simulator subject pool, through contact with local community, sporting and voluntary organisations, and through personal contacts within and outside the University of Leeds. Volunteers were paid to attend individual sessions lasting approximately one hour. Both younger and older samples were experienced drivers. However, as anticipated, the older sample had been driving for significantly longer (mean 43.1, SD 10.4 years) than the younger sample (mean 11.3, SD 5.8 years) (p < 0.001). The majority of volunteers were currently driving either every day (66.7 per cent of younger and 80 per cent of older sample) or two to three days a week (23.8 per cent of younger and 15 per cent of older drivers). Most drivers reported driving between 5000 and 10 000 miles per year (20 younger and 22 older). Nine younger (21.4 per cent) and eleven older drivers (27.5 per cent) reported annual mileage less than 5000 and five younger and two older drivers reported annual mileage in excess of 15 000 miles. There were no significant age differences among frequency of driving episodes or annual mileage (p > 0.05). Self-report measures Volunteers completed items relating to driving experience (number of years licence held, annual mileage and driving frequency), accident involvement and convictions for speeding, driving under the influence of drink/drugs and careless/dangerous driving (all within the last three years). Accidents were classified as minor (< £500 damage to vehicle/property AND no medical treatment) or major (either someone required medical treatment OR > £500 damage to vehicle/property OR both). Volunteers also completed a questionnaire pack that included the Driver Stress Inventory, the Driver Coping Questionnaire and the Driver Behaviour Questionnaire.
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The Driver Behaviour Questionnaire (DBQ: see Parker et al., 1995) measures driving errors (for example, underestimating the speed of an oncoming vehicle when overtaking), violations (for example, disregarding the speed limits) and lapses (for example, misreading signs and taking the wrong turn). British studies (for example, Parker et al. (2000); Parker et al. (1995a; 1995b) and in other countries (for example, Australia: see Blockley and Hartley (1995), Sweden: Aberg and Rimmo (1998)) broadly support this three factor structure. The split-half reliability of the DBQ in the current study was very good (r = 0.83). The Driver Stress Inventory (DSI: Matthews, Desmond, Joyner, Carcary and Gilliland, 1996) is a refinement of the Driver Behaviour Inventory (DBI: Gulian et al., 1989a). It measures driver stress vulnerability traits relating to aggression (for example, ‘it annoys me to drive behind a slow moving vehicle’), dislike of driving (for example, ‘I find myself worrying about my mistakes’) and alertness – redefined as hazard monitoring (for example, ‘I make an effort to look for potential hazards’). It also includes two new factors relating to thrill-seeking (for example, ‘I sometimes like to frighten myself a little while driving’) and fatigue proneness (for example, ‘I become inattentive to road signs when I have to drive for several hours’). The DSI has been found to be a valid and reliable measure of driver stress (Matthews et al., 1996). The split-half reliability of the DCQ in the current study was good (r = 0.71). The Driver Coping Questionnaire (DCQ: Matthews et al., 1996) was developed to assess individual differences in coping, by asking drivers how they deal with driving specifically when it is difficult or stressful and includes items concerning both explicit behaviours and internal psychological coping strategies. The DCQ measures five dimensions of coping: confrontive coping (for example, ‘showed other drivers what I thought of them’), task-focused (for example, ‘made sure I avoided reckless actions’), emotion-focused (for example, ‘blamed myself for getting too emotional’), reappraisal (for example, ‘tried to gain something worthwhile from the drive’) and avoidance (for example, ‘cheered myself up by thinking about things unrelated to the drive’). The DCQ has been found to be a valid and reliable measure of driver stress (Matthews et al., 1996). The split-half reliability of the DCQ in the current study was good (r = 0.7). Results Age differences in self-report assessments A series of t-tests were used to examine age differences in: (i) driver behaviour; (ii) driver stress and (iii) coping style. Younger drivers reported a relatively greater number of violations than older drivers, t(73.38) = –4.06, p < 0.001, but there were no age differences for errors or lapses. However, female drivers reported significantly more lapses compared to male drivers: t(80) = –2.53, p < 0.05 (controlling for age). With regard to driver stress, younger drivers reported higher levels of aggression, t(80) = –3.62, p = 0.001, and thrill-seeking, t(80) = –3.19, p < 0.01, whilst older drivers reported higher levels of hazard-monitoring, t(80) = 4.45, p < 0.001.
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There were significant age differences on three coping strategies. Younger drivers adopted a more confrontive coping strategy, t(74.85) = –4.57, p < 0.001, whereas older drivers placed a relatively greater emphasis on task-focused, t(79) = 2.95, p < 0.01, and reappraisal coping strategies, t(79) = 2.23, p < 0.05, to manage their driver stress. Age differences in emotion-focused and avoidance styles of coping with driver stress were non-significant. Gender differences were noted for emotionfocused coping (women scored higher), t(79) = –2.23, p < 0.05 (associated more with older females, partial r = 0.24, p < 0.05). Age differences in accidents and speeding Accident involvement was generally low and, given skewed distributions, age differences should be interpreted with caution. However, the younger sample reported significantly more major accidents than the older sample, t(51.173) = –2.26, p < 0.05. One older driver reported having one major accident. Seven younger drivers reported one, one driver reported two and another reported three major accidents. Eighty-five per cent of the older sample and 81 per cent of the younger sample reported no minor accidents. Five older and six younger drivers reported having one, one younger reported two and another reported four minor accidents (age differences non-significant). There was no significant age difference in speeding convictions (seven older and ten younger drivers reported speeding convictions). Associations between coping style and driver behaviour To examine associations between age group and coping style with respect to driving behaviours, driver stress and driving performance, a series of regression analyses was conducted. Each of the following was a dependent variable in a separate regression equation: (i) each DBQ scale; (ii) each DSI scale; (iii) minor and major accidents and (iv) speeding convictions. Given that older drivers had a greater level of driving experience than the younger sample, the effect of self-reported driving experience was ‘controlled’ by entering this first into each of the regression equations. Following this age group and ratings for a specific coping style were entered. Finally a vector representing the interaction of age group and coping style was entered. A summary of results for the regression equations is provided in Figure 11.1. Effects of age group have been reported above (although without controlling for driving experience). Here, we focus on the effects of coping style and the interactions between age group and coping style. Reappraisal coping was negatively associated with aggression (β = –0.26, p < 0.05) and violations (β = –0.22, p < 0.05). However, there were no significant interactive effects of age group. Task-focused coping was positively associated with hazard monitoring scores (β = 0.40, p < 0.001) and also speeding convictions (β = 0.28, p < 0.05). It was negatively associated with aggression (β = –0.42, p < 0.001), violations (β = –0.38, p < 0.001) and thrill-seeking (β = –0.22, p < 0.05). Interactive effects involving age group were not significant.
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Positive Association Reappraisal
Task Focus
Confrontive
Avoidance
Negative Association
Emotion Focus
Interaction
Hazard Monitor (DSI)
Aggression (DSI)
Violations (DBQ)
Thrill Seeking (DSI)
Speeding
Minor Accidents
Major Accidents
Dislike of Driving (DSI)
Fatigue Proneness (DSI)
Figure 11.1 Schematic of coping style and major associations with driving behaviour Confrontive coping was positively associated with aggression (β = 0.65, p < 0.001), violations (β = 0.49, p < 0.001) and thrill seeking scores (β = 0.53, p < 0.001) and minor accidents (β = 0.86, p < 0.05). There were no significant interactive effects involving age group. Avoidance coping was negatively associated with aggression scores (β = –0.23, p < 0.05) and positively associated with major accidents (β = 0.78, p < 0.05). The interaction between age group and avoidance coping was predictive of thrill-seeking behaviours (β = 0.99, p < 0.05), such that younger drivers with relatively lower avoidance coping scores were more thrill-seeking, whereas older drivers with relatively higher avoidance coping scores were relatively more thrill-seeking (see Figure 11.2). Emotion-focused coping was positively associated with dislike of driving (β = 0.72, p < 0.001) and fatigue proneness (β = 0.30, p < 0.01). The association with thrill seeking also approached significance (p = 0.052). Emotion-focused coping was also positively associated with speeding convictions (β = 0.27, p < 0.05). There was also a significant interaction between age group and emotion-focused coping style (β = 0.85, p < 0.05), such that younger drivers with relatively lower emotion-focused coping scores were more thrill-seeking, whereas older drivers with relatively higher emotion-focused coping scores were less thrill-seeking. Discussion This study examined age differences in driver behaviour, driver stress and driving performance. Associations between these variables were explored, and particular consideration was given to the potential influence of coping style on age differences in driving behaviour and driving stress.
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Figure 11.2 Interactions between: (i) age group and emotion-focused coping and (ii) age group and avoidance coping, for thrill-seeking. Solid regression line indicates the younger group Consistent with previous research (Aberg and Rimmo, 1998; Parker et al., 1995a; 1995b), younger drivers reported higher levels of violations (DBQ). They were also more aggressive and thrill-seeking in their driving (DSI). Aggression and thrillseeking are strongly associated with a confrontive coping style, with a concomitant reduction in task-focused coping for managing driver stress (Matthews et al., 1996). When considering coping styles, relative to older drivers, younger drivers adopted a more confrontive style. Confrontive coping is established as the most age-sensitive coping style and the strongest single predictor of dangerous driving behaviours; for example, violations and minor accidents (for example, Matthews et al., 1996). Results from the current study support these findings (Figure 11.1 above). Taken as a whole, this pattern of responses suggests that younger drivers are more ‘active/ external’ in their coping responses to driving situations. In contrast, older drivers reported relatively higher levels of hazard monitoring (DSI). Again, this is consistent with previous research (Matthews et al., 1996). When considered in combination with reports of relatively greater emphasis on ‘task focused’ and ‘reappraisal’ coping strategies, this suggests that older drivers adopt a more ‘passive/internal’ approach when coping with driving situations. Although age samples differed in levels of violations, there were no significant age differences in errors or lapses (DBQ). Again, this is reasonably consistent with existing findings from samples aged between 17 and 78 years of age (Parker et al., 1995a; 1995b; Reason et al., 1990). Errors and lapses do not appear to have a strong association with accidents even after controlling for the effects of age and gender (Matthews et al., 1997). This relationship may alter with increasing age. Lapses (for example, misreading signs and taking the wrong turning off a roundabout) were the most frequently reported behaviour when the DBQ was applied to an extended age range sample of 50 to 90 year olds (Parker et al., 2000), and were associated with accident involvement. Female drivers in the current study reported high scores for lapses. Misreading signs and ‘taking the wrong turning off a roundabout’ may not
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be considered a serious error unless it is carried out at the expense of safe driving practice. Older drivers report low levels of driver stress and of aggression with high levels of alertness (for example, Westerman and Haigney, 2000) and hazard monitoring (for example, Matthews et al., 1996), and in this manner their accident risk is reduced. Accident rates for the sample were generally low. However, younger participants reported having a greater number of major accidents over the three-year period preceding the study. In this study older drivers reported fewer major accidents and there was no significant difference in minor accidents. If self reported levels of deviant driving behaviours (for example, violations, accidents and convictions) were underreported due to impression management (Lajunen and Summala, 2003) or from memory loss (Loftus, 1993) this would be more likely to depress, rather than inflate, the effects demonstrated in the current study. Assuming that accident reports were accurate (see above) this suggests that self-reports of driving behaviour are reasonably reflective of ability (Parker et al., 2001). Following from the above, it may be that patterns of accident involvement are influenced by coping style to the extent that older experienced drivers adopt a more conscientious approach to driving, and younger drivers’ accident involvement may be related to increased propensity for driving violations (Parker et al., 1995a; 1995b). Driving experience gained over many years is likely to produce more advanced driving skills, and may be associated with a reduction in driver stress and mental workload (Patten et al., 2006), and therefore the possible confounding effect of driving experience was controlled in the current study. Older drivers may compensate for decline in driving and cognitive abilities both (i) ‘behaviourally’, for example, by not driving on busy roads or in rush hour traffic (McKnight and McKnight, 1999) and (ii) ‘psychologically’, for example, increasing levels of task-focused coping. Previous research efforts point to robust relationships between hazard monitoring (DSI) and task-focused coping (Matthews et al., 1996). These findings were supported in the current study. However, some associations between driving behaviour/performance and coping uncovered were not anticipated (see Figure 11.1 above). For example, task-focused coping was found to be associated with speeding convictions. This finding is counterintuitive as task-focused coping may be considered a coping strategy used to reduce driver error through increased alertness and concentration on the driving task, and its association with hazard monitoring (DSI). There were two significant interactions between coping style and age group with respect to the prediction of thrill seeking. These involved the ‘avoidance’ and ‘emotion focused’ coping styles. The nature of the interactions was very similar in both cases (see Figure 11.2 above). High scores on emotion-focused and avoidance coping scales tended to be associated with greater thrill seeking in the older group, but somewhat reduced thrill seeking in the younger group. Inappropriate or ineffective coping strategies could lead to both the experience and maintenance of higher levels of anxiety and stress while driving (Gulian et al., 1989b; McCrae and Costa, 1986). Given that older drivers report less thrill seeking the interactions (see Figure 11.2 above) are unusual. One possible explanation is that older experienced drivers do not perceive thrill seeking as risky driving behaviours (for example, Parker et al.,
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2001), thereby suffering from the generally accepted tendency of drivers to rate their quality of driving as being very good (for example, Groeger and Grande, 1996). This may have serious implications for such a safety-critical task as driving. Much of the work on driver stress and coping strategies to date has used samples of students and working adults (over 50). Indeed in Gulian et al. (1989a), the younger group were younger than 35 and the older group older than 46 years of age. The older age range in the current sample was between 60 and 80 years of age. It is possible that coping styles may interact with another variable that is related to age, for example, cognitive ability, although not necessarily the same variable for each coping style. A further possibility is that a coping style that is adaptive at one age might not be adaptive at another. Older drivers may maintain coping strategies that have become less effective or even adopt different less effective coping strategies with increasing age, and this may partly explain older driver error. The transactional model of coping developed by Gulian et al. (1989a; 1989b) and by Matthews et al. (1997) has proved to be a useful one for understanding driver stress reactions but may need to be extended to account for differences in driver stress with increasing age. Nonetheless, patterns of stress reactions broadly consistent with previous research were observed, helping to further clarify age differences in driver stress. This research has uncovered different patterns and interactions among coping styles used to manage driver stress, which may help us to explain age differences in driver behaviour and error, and therefore have implications for traffic safety. Older drivers’ stress responses and associated styles of coping are a worthwhile avenue for future research. Acknowledgements The first author has an E.S.R.C. studentship and also acknowledges financial support for volunteer recruitment from the Institute of Psychological Sciences, University of Leeds. Thanks also to Professor Gerry Matthews for copies of DSI and DCQ scales. References Aberg, L. and Rimmo, P.-A. (1998). ‘Dimensions of aberrant driving behaviour.’ Ergonomics, 41, 39–56. Anstey, K., Wood, J., Lord, S. and Walker, J. (2005). ‘Cognitive, sensory and physical factors enabling driving safety in older adults.’ Clinical Psychology Review, 25, 45–65. Blockley, P. and Hartley, L. (1995). ‘Aberrant driving behaviours: errors and violations.’ Ergonomics, 38, 1759–71. Chapman, P. and Underwood, G. (2000). ‘Forgetting near-accidents: the roles of severity, culpability and experience in the poor recall of dangerous driving situations.’ Applied Cognitive Psychology, 14, 31–44. Craik, F. and Jennings, J. (1992). ‘Human memory.’ In F. Craik and T. Salthouse (eds). The Handbook of Aging and Cognition. Hillsdale, NJ: Lawrence Erlbaum
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Associates Inc. Groeger, J. and Grande, G. (1996). ‘Self-preserving assessments of skill?’ British Journal of Psychology, 97, 61–79. Gulian, E., Matthews, G., Glendon, A., Davies, D.R. and Debney, L. (1989a). ‘Dimensions of driver stress.’ Ergonomics, 32, 585–602. Gulian, E., Debney, L., Glendon, A., Davies, D.R. and Matthews, G. (1989b). ‘Coping with driver stress.’ In F. McGuigan, W. Sime and J.M. Wallace, Stress and Tension Control, Plenum Publishing, 173–86. Hakamies-Blomqvist, L. (1994). ‘Compensation in older drivers as reflected in their fatal accidents.’ Accident Analysis and Prevention, 26, 107–12. Hasher, L. and Zacks, R. (1979). ‘Automatic and effortful processes in memory.’ Journal of Experimental Psychology: General, 108, 356–88. Kline, D. and Scialfa, C. (1996). ‘Visual and auditory aging.’ In J. Birren and K. Schaie (eds). Handbook of the Psychology of Aging (4th ed.). San Diego, CA: Academic Press. Lajunen, T. and Summala, H. (2003). ‘Can we trust self-reports of driving? Effects of impression management on driver behaviour questionnaire responses.’ Transportation Research Part F, 6, 97–107. Lazarus, R. and Folkman, S. (1984). Stress, Appraisal and Coping. New York: Springer. Loftus, E. (1993). ‘The reality of repressed memories.’ American Psychologist, 48, 518–37. Matthews, G., Desmond, P., Joyner, L., Carcary, B. and Gilliland, K. (1996). ‘Validation of the Driver Stress inventory and Driver Coping questionnaire.’ (Personal communication with first author, 2005.) Matthews, G., Desmond, P., Joyner, L., Carcary, B. and Gilliland, K. (1997). ‘A comprehensive measure of driver stress and affect.’ In T. Rothengatter and E. Vaya, Traffic and Transport Psychology: Theory and Application. Amsterdam: Pergamon. Matthews, G., Dorn, L. and Glendon, I. (1991). ‘Personality correlates of driver stress.’ Personality and Individual Differences, 12, 535–49. Matthews, G., Dorn, L. and Hoyes, T. (1992). ‘Individual differences in driver stress and performance.’ In T. Lovesey (ed.). Contemporary Ergonomics, London: Taylor and Francis. McCrae, R. and Costa, P. (1986). ‘Personality, coping and coping effectiveness in an adult sample.’ Journal of Personality, 54, 385–405. McKnight, A.J. and McKnight, A.S. (1999). ‘Multivariate analysis of age-related driver ability and performance deficits.’ Accident Analysis and Prevention, 31, 445–54. Parker, D., Lajunen, T. and Stradling, S. (1998). ‘Attitudinal predictors of aggressive driving violations.’ Transportation Research Part F, 1, 11–24. Parker, D., Macdonald, L., Rabbitt, P. and Sutcliffe, P., (2000). ‘Elderly drivers and their accidents: the aging driver questionnaire.’ Accident Analysis and Prevention, 32, 751–9. Parker, D., Macdonald, L., Sutcliffe, P. and Rabbitt, P. (2001). ‘Confidence and the
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older driver.’ Ageing and Society, 21, 169–82. Parker, D., Reason, J., Manstead, A. and Stradling, S. (1995a). ‘Driving errors, driving violations and accident involvement.’ Ergonomics, 38, 1036–48. Parker, D., West, R., Stradling, S. and Manstead, A. (1995b). ‘Behavioral characteristics and involvement in different types of traffic accident.’ Accident Analysis and Prevention, 27, 571–81. Patten, C., Kircher, A., Ostlund, J., Nilsson, L. and Svenson, O. (2006). ‘Driver experience and cognitive workload in different traffic environments.’ Accident Analysis and Prevention, 38, 887–94. Rabbitt, P. (1993). ‘Does it all go together when it goes? The 19th Bartlett Memorial Lecture.’ Quarterly Journal of Experimental Psychologist, 46A, 385–434. Reason, J., Manstead, A., Stradling, S., Baxter, J. and Campbell, K. (1990). ‘Errors and violations on the roads: a real distinction?’ Ergonomics, 33, 1315–32. Salthouse, T. (1992). ‘Influence of processing speed on adult age differences in working memory.’ Acta Psychologica, 79, 155–70. Stutts, J. (1998). ‘Do older drivers with visual and cognitive impairments drive less?’ Journal of the American Geriatrics Society, 46, 854–61. Westerman, S.J. and Haigney, D. (2000). ‘Individual differences in driver stress, error and violation.’ Personality and Individual Differences, 29, 981–98.
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PART 3 At Work Road Safety
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Chapter 12
Effects of Organisational Safety Culture on Driver Behaviours and Accident Involvement Amongst Professional Drivers Bahar Öz1 and Timo Lajunen2 University of Helsinki, Finland 2 Middle East Technical University, Turkey 1
Introduction International studies have shown that professional drivers are at a high risk of traffic accidents. For example, Salminen and Lähdeniemi (2002) indicated traffic as the most important cause of accidental deaths at work in Finland. Similarly, Bylund, Björnstig and Larson (1997) reported the professional drivers having the highest injury rate of employed people in Sweden. Also, Caird and Kline (2004) pointed out that about four workers per day are killed in on-the-job motor vehicle crashes in the United States. Although causes of traffic accidents have been studied extensively, there are only a few studies about traffic accidents during working hours (Salminen and Lähdeniemi, 2002; Caird and Kline, 2004). In addition to the general factors, there are particular work-related factors influencing professional drivers’ involvement in traffic accidents. Caird and Kline (2004) proposed that driver characteristics, environment, workplace, vehicle and organisation related factors influence professional drivers’ accident involvement. Different from non-professional drivers, organisational factors form an additional situational factor category for the professional drivers. The effects of organisation related factors (see Ostrom, Wilhelsen and Kaplan, 1993; Vredenburgh, 2002) and individual differences (Lajunen, 2001) on accident involvement have been demonstrated in literature. In their study about the organisational factors and on-the-job accidents of professional drivers, Caird and Kline (2004) investigated if work-related driving differed from free-time driving. One of the main differences between the nonprofessional and professional drivers is that driving is a less self-paced task for professional drivers whereas non-professional drivers can largely choose, for example, the mode of transportation, time of travel, route and target speed while driving. Hence, non-professional drivers can adjust the difficulty and risk level of the task whereas professional drivers’ work conditions are mostly predetermined. Among professional drivers, however, many different factors, like time schedule and long working hours, affect task demands. In addition, professional drivers have only limited influence on organisational demands and support.
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Organisational safety culture Organisations are thought to have different cultures (Schein, 1991). Organisational culture literature suggests that any organisation has organisational subcultures in addition to a general culture. In other words, organisational culture can be divided into components (Schein, 1991). According to Clarke (1999), talking about these small components is preferable to an investigation of the general culture since they can be measured and manipulated more easily than a general organisational culture. So far researchers have investigated organisational culture by dividing it into different components like creativity culture, motivation culture and safety culture. The key predictor of safety performance is believed to be the safety culture (Ostrom et al., 1993). Although safety culture concept is very critical for the organisations, there is no agreement about its definition or measurement among researchers (Cox and Flin, 1998). According to Lee (1993), safety culture includes shared commitment to think safely, behave safely, and trust in the safety measures put in place by the organisation. The literature has provided some concrete indicators of the existence of safety culture in a company. Wiegmann, Zhan and Von Thaden (2004) suggested that organisational commitment to safety indicates the level of safety culture because high commitment to safety by upper management provides resources for development and implementation of safety measures. If the safety culture of an organisation is well developed, the beliefs, attitudes and practices should emphasise minimising the exposure of employees to hazards. In other words, any type of application including training, selection, work schedules and use of equipment should be organised by taking employees’ safety into account. Varonen and Mattila (2000) found that a company’s attitudes to safety and its safety precautions are negatively correlated with accident rates. Similarly, Zohar (1980) suggested that employees’ perception about the company’s commitment to safety is the major indicator of safety culture. Driver behaviours In their classic study, Reason, Manstead, Stradling, Baxter and Campbell (1990) made a distinction between driver errors and violations. This differentiation provided a base for the development of the Manchester Driver Behaviour Questionnaire (DBQ; see Reason et al., 1990), which was developed for measuring aberrant driver behaviour by using self-reports. Reason and his colleagues (1990) showed that driver errors and violations are two empirically distinct types of behaviour. They defined errors as ‘the failure of planned actions to achieve their intended consequences’ and violations as ‘deliberate deviations from those practices believed necessary to maintain the safe operation of a potentially hazardous system’. Unlike errors, violations were seen as deliberate behaviours, although both errors and violations are potentially dangerous and could lead to a crash. Reason et al. (1990) also found a third DBQ factor, which they named ‘slips and lapses’. This factor included attention and memory failures, which can cause embarrassment but are unlikely to have an impact on driving safety (Parker, Reason and Stradling, 1995). Since errors and violations result from different psychological processes, they should be treated differently (Reason et al., 1990).
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Both errors and violations are negative behaviours in terms of intention or outcome. However, everyday traffic also involves other behaviours in which a driver’s intention is positive. These behaviours do not depend on regulations or rules. Taking care of the traffic environment or other road users, or helping them, is the main intention for these behaviours (Özkan and Lajunen, 2005). Özkan and Lajunen (2005) made an addition to the error-violation differentiation of the DBQ by introducing the concept of ‘Positive Driver Behaviours’ and developing a selfreport scale for measuring these behaviours. The main idea behind ‘Positive Driver Behaviours’ scale is that in addition to committing errors and violations, drivers also intend to help other road users and behave accordingly while driving. Driving includes both negative and positive driver behaviours at the same time. Aim of the study The aim of the present study was to investigate the effects of organisational safety culture components on driver errors, violations, positive driver behaviours, and accident involvement among professional drivers. Method Participants A total of 73 professional male drivers (38 taxi drivers and 35 cargo company drivers) volunteered to participate in the study. The participants were contacted through visits to the cargo companies and taxi stops, and they were assured of anonymity and confidentiality. The mean age of the drivers was 35.18 years (SD = 7.12) and the average annual mileage was 74.08 km (SD = 73.072). The participants had an average of 13.6 years’ driving experience, and the mean number of accidents they were involved in was 1.33. Questionnaires Organisational safety culture scale An organisational safety culture scale developed for the present study was used to collect information about the drivers’ perceptions of the safety culture of the company in which they were working. The scale consisted of 15 items measuring three safety culture dimensions including ‘traffic safety’, ‘general safety’ and ‘work safety’. The participants were asked to evaluate each item on a five point Likert type scale (1 = ‘strongly disagree’, 5 = ‘strongly agree’). Driver Behaviour Questionnaire (DBQ) To measure violations and errors, the DBQ with 28 items was used. Positive Driver Behaviours scale (Özkan and Lajunen, 2005) was used together with the DBQ. The Turkish translation and the factor structure of the DBQ have been validated in previous studies conducted among both professional (Sümer and Özkan, 2002) and non-professional drivers (Sümer,
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Lajunen and Özkan, 2002). The participants were asked to evaluate each item on a six point Likert type scale (1 = ‘never’, 6 = ‘always’). Alpha reliabilities of the scales were 0.85 for violations (12 items), 0.79 for errors (16 items) and 0.89 for positive behaviours (9 items). Background information Information related to age, sex, level of education, years of holding a full driving licence, experience, number of accidents, type of accidents and annual mileage were recorded. Results Factor structure of the organisational safety culture scale In order to determine the factor structure of the scale, factor analyses with principal axis factoring method were conducted for the 23 item scores of the organisational safety culture scale. Number of factors was determined by ‘mineigen > 1’ criterion, a screen plot and the conceptual relevance of the items to the dimension to which they should belong. As a result, three-factor solution was found to be the most interpretable one. Some items were left out from the final solution because of low factor loadings (< 0.30), cross loadings or being conceptually irrelevant to the dimension. Remaining items were re-evaluated and the most representative ones of each dimension in terms of the content were included into the last version of the scale (see Table 12.1). The first factor was named as ‘traffic safety’. This factor included seven items, and accounted for 39.4 per cent of the variance. The second factor was named as ‘general safety’ and included three items accounting for 9.1 per cent of the variance. The third factor was named as ‘work safety’ and it included five items, which accounted for 5.4 per cent of the variance. Internal consistency reliabilities for these three factors were 0.85, 0.74 and 0.92, respectively. Correlation analyses The DBQ scales (violations, errors and positive driver behaviours) and the organisational safety culture scales (traffic safety, general safety and work safety) based on factor analysis were calculated by averaging the item scores. Correlation coefficients between the sum variables and descriptive statistics can be found in Table 12.2. As Table 12.2 shows, the participants perceived the organisation that they were working for as being high in traffic safety, general safety and work safety. However, they tended to perceive the organisation’s general safety applications and attitude higher than that of traffic safety and work safety. Drivers scored relatively low on violations and errors whereas their positive driver behaviour scores were rather high. Correlation coefficients indicated that there was a positive relationship between traffic safety and work safety dimensions of the organisational safety culture. The error dimension of the DBQ was negatively related to general safety dimension
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Table 12.1 Factor structure of the organisational safety culture scale Items
Item-total correlation
Factors Factor 1 (Traffic safety)
In case of a danger while working, we can get immediate help from our company. The company which I am working for follows the traffic code and regulations closely and informs us about them. When hiring new employees the company which I am working for considers the safety principles. The company that I am working for takes other road users’ or customers’ complaints about the drivers into consideration. The company which I am working for emphasises safe driving more than being on time. The company which I am working for gives us training on topics like safe driving and first aid to improve our safety as drivers. The company which I am working for makes a special effort to do the technical check of the vehicles in time. Workload in the company is organized by not taking the safety of the drivers, customers and other road users into account. When the company’s requests lead to time pressure, there is nothing wrong with not obeying the safety regulations and rules. Safety is a secondary concern for the company in urgency. We can trust that we are safe while working because of the company’s work safety related policies. I find the work safety applications of the company satisfactory. The company which I am working for uses all the possible resources to make work safety guaranteed. In the company which I am working for there is tight control on obeying the safety related rules. In the company which I am working for, safety has the primary importance.
0.73
0.74
0.62
0.55
0.66
0.61
0.59
0.75
0.38
0.42
0.74
0.76
0.66
0.71
Factor 2 (General safety)
0.60
0.63
0.69
0.60
0.43
0.52
Factor 3 (Work safety)
0.74
0.69
0.89
0.84
0.82
0.82
0.79
0.69
0.78
0.72
Note: Factor loadings < 0.30 deleted for the sake of clarity.
148
Table 12.2 Correlations between the study variables
1. Age
8. Violations
SD
35.18
7.12
74 080
73 072
-0.13
1.33
1.56
–0.04
–0.08
3.34
1.03
–0.19
0.06
–0.18
4.31
1.24
0.02
0.20
–0.26*
–0.16
3.05
1.14
–0.18
0.04
–0.06
0.67**
–0.06
1.52
0.45
0.00
–0.12
0.07
0.11
–0.34**
1.81
0.68
–0.04
0.23
0.14
–0.22
–0.02
0.04
0.48**
1.39
0.08
0.09
0.02
–0.01
0.13
–0.10
–0.20
9. Positive driver 4.30 behaviours * p < 0.05, ** p < 0.01
1
2
3
4
5
6
7
8
0.27*
–0.17
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2. Annual km driven 3. Number of accidents 4. Traffic safety 5. General safety 6. Work safety 7. Errors
Mean
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and positively related to the work safety dimension of organisational safety culture. There was a positive relationship between errors and violations. The number of accidents drivers had been involved in had a negative relationship with the general safety dimension. As Table 12.2 indicates, positive driver behaviour dimension was not related to any of the variables. Regression analyses In order to investigate the effect of organisational safety culture on driver behaviour (errors, violations and positive driver behaviours) and accidents, four multiple regression analyses (errors, violations, positive driver behaviours and accidents as dependent variables) were conducted. In each analysis, the effects of age and mileage were controlled by forcing them into the model at the first step. In the second step, organisational safety culture dimensions were entered into the model. Table 12.3 shows the results of regression analyses. Traffic safety dimension of organisational culture was negatively related to violations and accidents. Strangely, both violations and errors were more frequent in the organisations in which workrelated safety was assessed to be high. General safety dimension was negatively related to both errors and accidents. However, none of the organisational safety culture dimensions predicted positive driver behaviours. Similarly, none of the DBQ dimensions predicted the number of accidents among professional drivers. Discussion The present study investigated the effects of organisational safety culture on driver behaviours and accident involvement among professional drivers. Results showed that safety culture predicted the frequency of errors, violations and accidents. Participants indicated that the organisation that they were working for arranged the workload by taking them, other drivers, customers, and the time pressure into account. The organisation gave priority to safety in every steps of work and used all the resources to guarantee safety in and outside the workplace. Reason (1997) suggested that two different processes are usually used to regulate factors causing accidents: production (for example, transportation of people or goods) and protection (for example, avoiding the occurrence and recurrence of accidents). Depending on the circumstances, one of these two factors is usually dominant, although the production approach is usually the dominant one because of several reasons. Firstly, production creates the resources that make protection possible. Secondly, managers are usually trained for production and are, therefore, more skilled in production than in protection. Thirdly, the quality of production can be directly evaluated whereas the quality of protection is usually reflected indirectly as lack of negative outcomes (such as an accident). Drivers in the present study indicated their workplace as safety oriented by emphasising that protection related applications and approach were dominant in the organisation. These applications included, for example, safety training and providing information about the traffic related regulations and laws. As the results showed, protection focused applications
150
Table 12.3
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Regression of organisational safety culture on DBQ scales and accident involvement
Independent R2 Adj R2 R2 Change Variables Violations as a dependent variable 1. Age – Annual km 0.05 0.02 0.05 driven 2. Traffic safety General 0.19 0.12 0.14 safety Work safety Errors as a dependent variable 1. Age Annual km 0.01 –0.02 0.01 driven 2. Traffic safety General 0.22 0.16 0.21 safety Work safety Positive Driver Behaviours as a dependent variable 1. Age Annual km 0.03 –0.00 0.03 driven 2. Traffic safety General 0.06 –0.02 0.03 safety Work safety Number of accidents as a dependent variable 1. Age Annual km 0.03 –0.01 0.03 driven 2. Traffic safety General 0.13 0.06 0.11 safety Work safety * p < 0.05, **p < 0.01 Step
F
df
1.60
2
2.87*
5
0.46
2
3.47**
5
0.97
2
0.71
5
0.82
2
1.89
5
β
–0.03 0.21 –0.50** –0.14 0.37*
0.01 –0.12 –0.20 –0.34** 0.40**
0.15 0.10 0.16 0.08 –0.18
–0.13 –0.11 –0.30* –0.29* 0.14
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resulted in fewer violations. If organisation’s ‘cultural message’ is that production – not people or safety – is the priority, employees might perceive organisation’s commitment to safety loose, which, in turn, may affect their safety performance (Vredenburgh, 2002). In the present study, general safety had a negative relationship to self-reported accidents and errors. This finding is in line with those of Vuuren (2000), who showed that improvement in company’s safety climate resulted in a significant decreases in accident rate. Similarly, Kirschenbaum, Oigenblick and Goldberg (2000) reported that hazardous working conditions increased chances of being involved in an accident. Moreover, Hayes, Perander, Smecko and Trask (1998) stated that positive employee perceptions of safety are related to low accident rates. Results of the study indicated a positive relationship between high work safety (evaluated by drivers) and self-reported number of errors and violations. In other words, if the drivers found the company’s safety regulations satisfactory, company’s investment in safety sufficient and safety as a priority, they reported more errors and violations. One explanation for this slightly surprising finding is that a work safety oriented company usually encourages its employees to report incidents and hazardous situations. Hence, a company’s open and blame-free atmosphere might actually make it easier for drivers to report their errors and violations. Earlier studies have shown that communication between the company and employees increases safe behaviours. Hofmann and Stetzer (1996) showed that employees working with a supervisor not mentioning safety might perceive safety as unimportant. If the involvement of management is high, that is, if the management gets involved in critical safety activities including seminars and training days, this provides good communication of safety related topics in the organisation (Wiegmann et al., 2004). Based on these studies, it can be claimed that good communication between management and the drivers increases the likelihood of reporting unwanted behaviours (errors and violations) and risky situations because drivers trust that the company is going to adopt the precautions it needs. The present study showed that the company’s safety culture is an important factor in terms of professional drivers’ traffic safety. This study focused mainly on three aspects of organisational safety culture. In future studies, the effects of communication, training and reward systems on professional drivers’ safety behaviour and outcomes should be studied. Acknowledgements This study was supported by The Scientific and Technological Research Council of Turkey (TÜBİTAK Project No: 103K017) and Marie Curie Transfer of Knowledge programme (‘SAFEAST’ Project No: MTKD-CT-2004-509813).
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References Bylund, P.O., Björnstig, U. and Larsson, T.J. (1997). ‘Occupational road trauma and permanent medical impairment.’ Safety Science, 36(3), 187–200. Caird, K.J. and Kline, T.J. (2004). ‘The relationships between organisational and individual variables to on-the-job driver accidents and accident-free kilometers.’ Ergonomics, 47(15), 1598–1613. Clarke, S. (1999). ‘Perceptions of organisational safety: implications for the development of safety culture.’ Journal of Organisational Behaviour, 20(2), 185–98. Cox, S. and Flin, R. (1998). ‘Safety culture: philosopher’s stone or man of straw?’ Work and Stress, 12, 189–201 Lee, T.R. (1993). ‘Seeking a safety culture.’ Atom, 429, 20–3. Ostrom, L., Wilhelmsen, C. and Kaplan, B. (1993). ‘Assessing safety culture.’ Nuclear Safety, 34, 163–72. Özkan, T. and Lajunen, T. (2005). ‘A new addition to DBQ: positive driver behaviours scale.’ Transportation Research Part F, 8, 355–68. Hayes, B.E., Perander, J., Smecko, T. and Trask, J. (1998). ‘Measuring perceptions of workplace safety: development and validation of the work safety scale.’ Journal of Safety Research, 29(3), 145–61. Hofmann, D.A. and Stetzer, A. (1996). ‘A cross-level investigation of factors influencing unsafe behaviours and accidents.’ Personnel Psychology, 49, 307– 39. Kirschenbaum, A., Oigenblick, L. and Goldberg, A.I. (2000). ‘Well being, work environment and work accidents.’ Social Science and Medicine, 50, 631–9. Parker, D., Reason, J., Manstead, A. and Stradling, S. (1995). ‘Driving errors, driving violations and accident involvement.’ Ergonomics, 38, 1036–48. Reason, J., Manstead, A., Stradling, S., Baxter, J. and Campbell, K. (1990). Ergonomics, 33(10/11), 1315–32. Reason, J. (1997). Organisational Accidents. Manchester: Manchester University Press. Salminen, S. and Lähdeniemi, E. (2002). ‘Risk factors in work-related traffic.’ Transportation Research Part F, 5, 77–86. Schein, E.H. (1991). Organisational Culture and Leadership (2nd ed.). San Francisco: Jossey-Bass Publishers. Sümer, N., Lajunen, T. and Özkan, T. (2002). ‘Sürücü davranışlarının kaza riskindeki rolleri: İhlaller ve hatalar.’ Traffic and Road Safety International Congress, 8–12 May, Gazi University, Ankara, Turkey. Sümer, N. and Özkan, T. (2002). ‘Sürücü Davranışları, Becerileri, Bazı Kişilik Özellikleri ve Psikolojik Belirtilerin Trafik Kazalarındaki Rolleri.’ TürkPsikoloji Dergisi, 17(50), 1–22. Varonen, U. and Mattila, M. (2000). ‘The safety climate and its relationship to safety practices, safety of the work environment and occupational accidents in eight wood-processing companies.’ Accident Analysis and Prevention, 32, 761–9. Vredenburgh, A.G. (2002). ‘Organisational safety: which management practices are most effective in reducing employee injury rates?’ Journal of Safety Research,
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33, 259–76. Vuuren, W. (2000). ‘Cultural influences on risks and risk management: six case studies.’ Safety Science, 34, 31–45. Wiegmann, D.A., Zhang, H., Von Thaden, T.L., Sharma, G. and Gibbons, A.M. (2004). ‘Safety culture: an integrative review.’ The International Journal of Aviation Psychology, 14(2), 117–34. Zohar, D. (1980). ‘Safety climate in industrial organisations: theoretical and applied implications.’ Journal of Applied Psychology, 65, 96–102.
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Chapter 13
Stages of Change in the Australian Workplace and its Application to Driver Education Tamara Banks, Jeremy Davey and H. Biggs Centre for Accident Research and Road Safety, Queensland, Australia Introduction High social and financial costs are currently being incurred by both industry and society as a result of work-related road incidents. It is estimated that worldwide 50 million people are injured and an additional 1.2 million people are killed annually in road crashes (WHO, 2004). More specifically it has been estimated that in Australia, the total cost of work-related road incidents may be in the range of $1 billion to $1.5 billion per annum (Wheatley, 1997) and that the average total insurance cost of a fleet incident is approximately $28 000 with costs incurred to both the company and society (Davey and Banks, 2005). Additionally, motor vehicle incidents are the most common mechanism for Australian compensated fatalities, representing 35 per cent of all work compensated deaths (ASCC, 2006). In an attempt to reduce the frequency and severity of work-related road incidents, some organisations implement driver education. Driver education aims to improve driver knowledge, attitudes and behaviour. The term is used broadly to cover a range of instruction and learning procedures that emphasise the cognitive processes and underlying values relating to safe driving behaviour. Although many practitioners in the occupational health and safety field assume that driver safety can be enhanced through the provision of driver safety education, research investigating this relationship has found mixed results. For example, driver education has been found to be associated with greater caution when approaching and overtaking a hazard, greater adherence to traffic signals and signs, greater visual monitoring of the driving environment, fewer traffic violations/ infringements, lower risk acceptance and lower accident risk (Chapman, Underwood and Roberts, 2002; Dorn and Barker, 2005; Llaneras, Swezey, Brock, Rogers and Van Cott, 1998; Lund and Williams, 1985). Comparatively, other studies have failed to find significant risk reductions in relation to the provision of driver education (Christensen and Glad, 1996; Katila, Keskinen, Hatakka and Laapotti, 2004; Lynam and Twisk, 1995). It is suggested that the observed contradictory findings from past studies may be partially explained by variations between participants’ readiness for change. The stages of change model, also known as the transtheoretical model of change (Prochaska, DiClemente and Norcross, 1992), is a behaviour change model that
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offers a framework for understanding variations in readiness for change. The model suggests that individuals require different types of support based on their position within the change process as they pass through the cyclical phases of precontemplation (not thinking of changing one’s behaviour), contemplation (seriously considering changing one’s behaviour in the near future), preparation (making plans and intending to change one’s behaviour in the very near future, may have started to make minor changes), action (engaged in changing one’s behaviour) and maintenance (working to consolidate gains from one’s changed behaviour and prevent relapse). Originally developed in the field of psychotherapy for studying individual behaviour change, the stages of change model has strong empirical support in the area of individual health promotion. For example, research across a range of behaviours including dietary behaviour (Campbell, et al., 1994) and mammography screening (Rakowski, et al., 1998) has found that stage-matched interventions were more effective in changing behaviour than ‘one size fits all’ interventions. This makes intuitive sense considering that research comparing stage distributions across a range of health-related behaviours has found that in pre-action individuals approximately 40 per cent of the population are in the precontemplation phase, 40 per cent are in the contemplation phase and only 20 per cent are in the preparation phase (Velicer et al., 1985; Laforge, Velicer, Richmond and Owen, 1999). This distribution pattern suggests that a combination of initiatives targeting awareness raising, discussing how changes could be made and making plans to change may be an effective approach to improving work-related road safety. It also may account for previous research findings that driver training is not effective. If driver training is focused as a single action-orientated initiative and has not prepared individuals by creating the conditions necessary for change, it may only be catering to the needs of approximately 20 per cent of pre-action employees. The stages of change model could be used in organisations to design driver training programs that are appropriate to the readiness for change of managers and employees. In recent years the model has started to be applied internationally to organisational change in the areas of ergonomics and health promotion (Haslam, 2002; Prochaska, Prochaska and Levesque, 2001). A case study of health and safety appraisal within an English manufacturing company identified that the Stages of Change model provided a useful framework for assessing attitudes and beliefs and assisting in recognising individual and organisational readiness to change (Barrett, Haslam, Lee and Ellis, 2005). Additionally, the model provided a framework for explaining the observed effectiveness of a cattle handling injury prevention program in New Zealand. Interviews with approximately 1500 farming personnel revealed that awareness raising methods including leaflets and videos were most effective in transitioning farmers from contemplation to action. Alternatively, field days that provided farmers with an opportunity for tailored advice were more effective in transitioning farmers from action to maintenance (Slappendel, 2001 as cited in Haslam, 2002). This chapter expands upon recent organisational behaviour change research by exploring the utility of the stages of change model as a framework for understanding employee road safety behaviour change. Firstly, it is hypothesised that the stages of change model will provide a framework for identifying employee readiness to engage in work-related road safety behaviour change. Secondly, it is hypothesised
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that the model will provide a framework for explaining employees’ perceived effectiveness of work-related road safety initiatives. Finally, this chapter suggests how practitioners can apply the stages of change framework to tailor driver training programmes to most effectively meet client needs. Method Participants To allow research findings to be generalised to a wide range of organisations, two contrasting organisations were recruited to participate in this research. Organisation A was a not-for-profit state-based provider of residential and community services. It employed approximately 2000 staff and was supported by a network of approximately 500 volunteers. Organisation A operated a fleet of approximately 200 vehicles. In comparison, Organisation B was a for-profit national utilities provider with a workforce of approximately 35 000 employees. The organisation was jointly owned by the Australian government and private shareholders. Organisation B operated a fleet of approximately 15 000 vehicles. Both organisations serviced customers in urban, rural and remote areas of Australia and therefore required their employees to operate vehicles in a range of environments. Interviews were conducted with five employees from each of the organisations. The selection of participants was a convenience sample and was ultimately determined by the employer. Efforts however were made to obtain a random and representative sample within this real-world context. Participating organisations provided access to both male and female employees from a range of roles and levels of seniority within their organisation. Participants’ roles included fleet manager, occupational health and safety manager, department manager and operational employees who were required to drive as part of their work. Interviews A semi-structured interview schedule was developed to explore the utility of the stages of change model as a framework for understanding employee road safety behaviour change. Previous research suggests that it is possible to assess stage of change via individuals’ responses to a small number of questions (Haslam, 2002; Haslam and Draper, 2000). Based on adaptations from previous research (Barrett, Haslam, Lee and Ellis, 2005; Whysall, Haslam and Haslam, 2006), combinations of open and closed questions were developed. In some cases several questions were asked at each stage to elicit sufficient information to identify employees’ stage of change. Interview participants were also asked to comment on any work-related road safety initiatives they were aware of that their organisation was intending to or was already engaging in. The formality and depth of interview questions was varied to suit the employees’ level of seniority and involvement in driver safety initiatives. The following core questions were asked in this order until a negative response was
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obtained. The last positive response indicated the employee’s stage of readiness for change. 1. Are you aware of any work-related road safety risk? (Yes = continue, No = precontemplation) 2. Are you planning to take any action to reduce work-related road safety risk in the next six months? (Yes = continue, No = precontemplation) 3. Do you have any definite plans to reduce work-related road safety risk in the next month? (Yes = continue, No = contemplation) 4. Have you taken any deliberate action to reduce work-related road safety risk? (Yes = continue, No = preparation) 5. Are you currently doing anything to maintain work-related road safety within your organisation? (Yes = maintenance, No = action). To explore whether employees’ perceived effectiveness of safety initiatives was related to their stage of change, interview participants were asked to comment on any work-related road safety initiatives they had experienced in their organisation. Several steps were taken to maximise the integrity of the interview data collected. Firstly, the interview schedule was piloted and refined based on feedback from employees not participating in the main study. Secondly, interviews were conducted face-to-face in a private office on the premises of each organisation to minimise distractions and misinterpretations of information. Thirdly, employees were interviewed individually to minimise any contamination of data arising from potential group bias. Fourthly, it was stressed that participation was voluntary and confidential to encourage participants to openly report their beliefs and behaviours. Finally, consent was sought from participants for the interview to be recorded and notes to be taken during the session. All recorded data were transcribed verbatim to ensure accuracy. Analysis A three-phase approach as described by Miles and Huberman (1994) was adopted to analyse the transcribed data. Firstly, data was organised via cutting and pasting material into meaningful collections that corresponded with the interview questions. Secondly, emerging themes were identified and patterns within and between themes were explored. This phase involved summarising the data under each theme and selecting verbatim quotes to illustrate the themes. Thirdly, conclusions were drawn after interpretations of the data were verified against the interview transcripts and existing literature. Results An analysis of the interview transcripts suggests that the five core questions provided a useful starting point for classifying employees’ stage of change. However, in this research the five questions were not sufficient to distinguish between adjoining stages
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for some participants. With the inclusion of additional probing questions it was identified that the stages of change model could provide a framework for classifying employee readiness to engage in work-related road safety behaviour change. Organisation A Within Organisation A, it was identified that the observed stage of readiness varied between individuals at the management level and between the levels of managers and employees. Interviews indicated that managers (n = 3) varied between the preparation and action stages of readiness. All managers indicated an awareness of their organisation’s exposure to work-related road safety risks. Reported risks included both general road safety risks and risks that could be considered specific to their organisation. Generic risks reported included wildlife, visibility, road trains, fatigue and high exposure with reports of some employees driving up to 70 000km per annum. Organisation specific risks pertained more to the transporting of clients. For example one manager reported that some clients ‘…might just grab the handbrake or grab the wheel. We’ve had a couple of grab the wheel situations. We had a towel over the head the other day while the driver was driving. So we have a few young people that pose a risk with our cars.’ Although all managers believed that that their views of risk were shared by other managers, some felt that employees working at a lower level of the organisation may not be aware of the risks. For example when talking about road safety initiatives one manager commented, ‘I think management will embrace it, but the next couple of levels will struggle because the people I know are still saying we’re about doing this and our cars getting dinted. You know, “I was in a hurry. Doesn’t really matter…”.’ At an organisational level, managers’ comments indicated desires and intentions to enhance work-related road safety using a range of initiatives in the very near future. For example, ‘I want to have a new fleet management system where we do record the data that will enable us to manage and then have appropriate consequences for their actions…’ and ‘…on my “to do” list is writing a fleet policy.’ These comments would suggest that managers are currently operating in the preparation stage for some aspects of driver safety. It was also reported that some driver safety initiatives have recently been introduced in the organisation. These initiatives included, for example, consideration of safety features when selecting vehicles, monitoring vehicle servicing, fitting cargo barriers in all station wagons and promoting road safety. Managers reported engaging in some work-related road safety behaviours at a personal level; for example, ‘watch other drivers is the first thing I always do’. At an organisational level, examples include ‘making sure you haven’t got projectiles in the car and when you’re hanging clothes up, to hang them on the right rear, which I just can’t tell you how many people I keep pulling up on that. They hang them on the left rear and if you’re looking to see who’s on your left, there’s no way. It totally blocks your vision’ and ‘we have four-wheel drives in some of our residences. So we’ve had our staff in those houses do four-wheel drive classes and road safety classes as part of their induction.’ These comments would suggest that managers are currently operating in the action stage for some aspects of driver safety.
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Managers agreed that while some steps have been taken in the last couple of months, that there was room for improvement in the management of work-related road safety risks within their organisation. This is illustrated by one manager’s comment that ‘I think we’ve got a long way to go in terms of providing better training and information to people who are driving’. When asked about work-related road safety initiatives in their organisation, the managers reported mixed beliefs about the effectiveness of current awarenessraising initiatives. Based on previous discussions with subordinates, one manager believed that the regular road safety posters, emails and information provided on their organisation’s intranet was effective because it increased awareness without forcing employees to go to safety meetings or training. This manager commented that ‘our staff don’t talk about what they learnt at the four-wheel drive course, but you’ll go to a staff meeting and someone will say, “Hey, have you seen that thing about us (on the intranet)” – it’s like, oh my god, you read it.’ In comparison, another manager believed that the effectiveness of information bulletins to raise awareness of road safety risk was limited. This manager noted that ‘There’s been a couple of information bulletins go out on it, but I’m not actually, I can’t recall right now what was actually in it. I don’t think people stop to take a lot of notice of it.’ Managers also identified a lack of understanding within the organisation as a key barrier in trying to implement road safety initiatives. Managers felt that vehicle incidents were not currently treated as seriously as other health and safety incidents and anticipated resistance when attempting to introduce a vehicle incident monitoring database. This was illustrated through comments such as ‘it will be why do you want to make the people do that? That’s extra work, you know. All they did was reverse into a post, you know. They didn’t hurt anybody.’ Within Organisation A, the interviews indicated that the two employees were operating in the pre-contemplation stage of readiness. Unlike the managers, who recognised both general and organisation-specific road safety risks, only general road safety risks were acknowledged by the employees. Reported risks included driving long distances, mobile phone use, high volume of traffic on the roads and other drivers lacking good driving ability. It was also noted that management must consider road safety risks to be a significant issue because it was discussed at a quarterly senior meeting. Despite both employees being aware of driving risks, neither employee reported intentions to change their driving behaviour. For example one employee commented that ‘I haven’t had any damage to the car in the last seven years. So I’ve done pretty well. So I can’t see any way of improving what I do as such. That would probably change if I had an accident.’ When asked to comment on any work-related road safety initiatives they had experienced in their organisation, the employees reported the presence of many road safety posters displayed in their workplace. While they thought these were beneficial, they were not considered to be sufficient. For example one employee commented: you can’t just send out an e-mail, put up a poster. You really, I mean, ultimately, people should be given specific training, driver instruction for change to happen, people have to be aware of the need for change, they have to have a desire for change and they have to know – and then they have the knowledge of how to change. Then they have to have
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the ability of how to change and then they have to have some reinforcement to make the change stay to stop them bouncing back to the old way.
Organisation B Within Organisation B the interviews indicated that managers (n = 2) and employees (n = 3) were operating in the maintenance stage of readiness. Both managers and employees indicated an awareness of their organisation’s exposure to work-related road safety risks. Reported risks included both general road safety risks and risks that could be considered specific to their organisation. Generic risks reported included wildlife, poor road conditions, fatigue, driving in isolated areas, travelling long distances and reduced concentration when answering phone calls via hands-free kits. Organisation specific risks pertained more to the nature of the work demands. For example one manager recognised that employees ‘might get called out of bed at 2 or 3 o’clock in the morning to drive long distances so sleep deprivation comes into it’. Both managers and employees reported that their organisation has continued to roll out road safety initiatives for several years. Examples of reported initiatives include a driver awareness campaign featuring stickers on cars stating that the organisation values safety, a driver profiling tool to identify high risk drivers, following employees up on infringement data, considering safety features when selecting vehicles, monitoring vehicle servicing, fitting cargo barriers in all station wagons and enhancing awareness by featuring road safety posters, promoting road safety around holidays and including fleet safety topics in the monthly fleet newsletter. Managers additionally commented on plans to continue enhancing road safety behaviours by updating current initiatives and continuing to introduce new initiatives. Further evidence that Organisation B was operating in the maintenance stage was illustrated by employee comments pertaining to engaging in road safety behaviours. Reported behaviours included using cruise control to manage speeding, slowing down to allow for kangaroos potentially crossing the roads in the afternoons, taking a slower more careful approach when in the city, performing safety checks on vehicles and actively participating in the monthly health and safety meetings. When asked about work-related road safety initiatives in their organisation, both managers reported that driver safety was considered to be a health and safety issue and that they perceived current safety initiatives to be effective. For example: ‘I think it’s just a matter of improving on what we’ve already got’ and ‘they’re amongst the best that you’ll see, no doubt about that’. Alternatively, employees had mixed opinions about the effectiveness of current initiatives. For example, one employee described how they believed that the current safety meetings were working well as they provided a great opportunity to present and trouble shoot safety concerns as a team. Another employee reported that: [S]afety changes normally impact with a negative. Normally, changes will be to not drive as long or far, but increased work loads always conflict. All layers of management are aware of the situation about staff shortages and extra distances to travel. I believe the company does have a commitment to driver safety but is willing to overlook its own policy when it comes to a situation of resources and money.
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This statement would suggest that current initiatives provided limited advice on how to juggle the potentially conflicting requirements of production and safety. Discussion This chapter demonstrates that the stages of change model provides a useful framework for understanding employee readiness for road safety behaviour change. Additionally, perceptions pertaining to initiative effectiveness were found to vary in relation to an individuals’ stage of readiness. More specifically in Organisation A, although managers were operating within the preparation and action stages, employees were operating in the pre-contemplation stage. Managers identified employee resistance to implementing action initiatives as a potential barrier. The stages of change model would suggest that employee resistance to change may occur when safety initiatives are not targeted at an appropriate level for employees’ readiness for change. The selection of an inappropriate level may arise due to differences in readiness for change between managers and employees. When rolling out safety initiatives, managers have often previously spent much time in the contemplation and preparation phases. Understandably, managers are then ready for action and often attempt to impose action initiatives upon employees. Alternatively, employees may not have previously considered the risks of current practice or the benefits of new safety initiatives. They are often not prepared for change and are therefore slow to respond or may even resist the change initiatives. Individuals in Organisation A generally believed that current initiatives aimed at increasing awareness of road safety risks were beneficial. However they were not considered to be sufficient. Based on the stages of change model it is suggested that safety initiatives in Organisation A should continue enhancing current understanding and commitment, but also go further in providing practical information and support to transition employees from a pre-contemplative stage towards action. In comparison, in Organisation B both managers and employees were operating in the maintenance stage of readiness. Overall, managers and employees perceived the organisation’s current safety initiatives, including ongoing advice and practical information, to be effective. However, barriers to maintaining safe driving were identified, including conflicts arising between reaching production targets with increased workloads while adhering to safety policies and procedures. Based on the stages of change model it is suggested that safety initiatives in Organisation B should further support employees to achieve and maintain safe driving behaviour through methods such as ongoing advice about how to resolve driving safely when faced with competing production goals, performance feedback and skills training. The organisation should also continue to monitor employees’ behaviour to identify any early signs of behaviour relapse. Based on this research it is suggested that the stages of change model could be used by practitioners to design driver behaviour and training methods that are appropriate to managers and employees’ readiness for change. Examples of how the stages of change framework could be applied to guide the design of driver education are outlined below. In the pre-contemplation stage employees would see no problem
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with their current road safety behaviour and express no intention to change. In this stage individuals need to be persuaded that unsafe driving behaviour must be addressed. The model would suggest that attempts to impose action orientated driver education on pre-contemplative individuals may only achieve partial success as a personal understanding of the risks and a desire to engage in safe behaviours has not first been achieved. To help transition employees from a pre-contemplative to a contemplative stage, driver education should focus on raising awareness of workrelated driving risks. In the contemplation stage employees would be aware of the risks associated with work-related driving and the need to adopt safe behaviours. Contemplative individuals would be making long term plans to reduce and manage their road safety risk. To help transition employees from a contemplative to a preparation stage, driver education should focus on providing educational material designed to reinforce their motivation to adopt safe behaviours and outline what is involved in adopting safer driving behaviours. In the preparation stage employees would be intending to take action in the very near future. To help transition employees from a preparation to an action stage, driver education should focus on providing practical information and support in learning new skills. Barriers to change should be resolved, and individuals should be encouraged to make specific plans through goal setting or contracting to foster employee commitment and ownership of safe driving behaviours. In the action stage employees would be modifying their behaviour or environment to manage work-related road safety risks. Individuals in this stage require support to achieve new safety behaviours and to maintain modified behaviours. To facilitate commitment to the modified behaviours and help transition employees from an action to a maintenance stage, driver education should focus on providing ongoing advice, feedback and skills training. In the maintenance stage employees would have been engaging in safety behaviours over a prolonged period of time. To facilitate employees remaining in the maintenance stage, driver education should focus on consolidating the gains made and preventing relapse. This can be achieved through the provision of ongoing advice, feedback and training and the monitoring of employees for early signs of behaviour relapses. It is important to note that employees may relapse to an earlier stage regardless of their current stage of change. To target relapsed employees that have failed to continue engaging in work-related road safety behaviours, driver education should support the progression back through the stages towards action and maintenance. Driver education should aim to discover the barriers that led to the employee ceasing safe practices and to motivate the employee to re-engage in safe practices through the provision of tailored information, training and feedback. This chapter builds upon previous literature pertaining to driver education by identifying how the stages of change model can be applied as a useful framework to guide the development of driver education programmes. Based on the model, it is suggested that providers of driver education for organisations could make a brief assessment during project negotiation of managers and employees readiness for change. Based on their assessment, providers could then determine the most
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appropriate structure and content of driver education to effectively meet the needs of their client. By adopting a stage-matched approach to driver education, providers may be able to reduce change resistance and accelerate employee movement towards the action and maintenance stage of work-related road safety behaviours. Due to the small participant sample size, further research should be conducted to validate these findings with a larger number of employees within case study organisations and across a greater range of organisations to enhance the generalisability of research findings. References Australian Safety and Compensation Council (2006). Compendium of Workers Compensation Statistics Australia 2003–04. Canberra: Commonwealth of Australia. Barrett, J.H., Haslam, R.A., Lee, K.G. and Ellis, M.J. (2005). ‘Assessing attitudes and beliefs using the stage of change paradigm: case study of health and safety appraisal within a manufacturing company.’ International Journal of Industrial Ergonomics, 35, 871–87. Campbell, M.K., DeVellis, B.M., Strecher, V.J., Ammerman, A.S., DeVillis, R.F. and Sandler, R.S. (1994). ‘Improving dietary behaviour: the effectiveness of tailored messages in primary care settings.’ American Journal of Public Health, 84, 783–7. Chapman, P., Underwood, G. and Roberts, K. (2002). ‘Visual search patterns in trained and untrained novice drivers.’ In Transportation Research Part F: Traffic Psychology and Behaviour, 5(2), 157–67. Christensen, P. and Glad, A. (1996). Mandatory Course of Driving on Slippery Roads for Drivers of Heavy Vehicles: The Effect on Accidents. TOI Report 334/1996. Oslo: Transpotokonomisk institutt. Davey, J. and Banks, T. (2005). ‘Estimating the cost of work motor vehicle incidents in Australia.’ Paper presented at the Australasian Road Safety Research Policing Education Conference, Wellington, New Zealand. Dorn, L. and Barker, D. (2005). ‘The effects of driver training on simulated driving performance.’ Accident Analysis and Prevention, 37(1), 63–9. Haslam, C. and Draper, E.S. (2000). ‘Stage of change is associated with assessment of the health risks of maternal smoking among pregnant women.’ Social Science Medicine, 51, 1189–96. Haslam, R.A. (2002). ‘Targeting ergonomics interventions: learning from health promotion.’ Applied Ergonomics, 33, 241–9. Katila, A., Keskinen, E., Hatakka, M. and Laapotti, S. (2004). ‘Does increased confidence among novice drivers imply a decrease in safety? The effects of skid training on slippery road accidents.’ Accident Analysis and Prevention, 36, 543– 50. Laforge, R.G., Velicer, W.F., Richmond, R.L. and Owen, N. (1999). ‘Stage distributions for five health behaviours in the USA and Australia.’ Preventative Medicine, 28, 61–74. Llaneras, R.E., Swezey, R.W., Brock, J.F., Rogers, W.C. and Van Cott, H.P. (1998).
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‘Enhancing the safe driving performance of older commercial vehicle drivers.’ International Journal of Industrial Ergonomics, 22(3), 217–45. Lund, A.K. and Williams, A.F. (1985). ‘A review of the literature evaluating the defensive driving course.’ Accident Analysis and Prevention, 17(6), 449–60. Lynam, D. and Twisk, D, (1995). Car Driver Training and Licensing Systems in Europe. TRL Report 147. Crowthorne, UK: Transport Research Library. Lynn, P. and Lockwood, C. (1998). The Accident Liability of Company Car Drivers. TRL Report 317. Berkshire: Transport Research Laboratory. Miles, M.B. and Huberman, A.M. (1994). An Expanded Source Book: Qualitative Data Analysis. London: Sage Publications. Prochaska, J.M., Prochaska, J.O. and Levesque, D.A. (2001). ‘A trans-theoretical approach to changing organizations.’ Administration and Policy in Mental Health, 28(4), 247–61. Prochaska, J.O., DiClemente, C.C. and Norcross, J.C. (1992). ‘In Search of How People Change: Applications to Addictive Behaviours.’ American Psychologist, 47(9), 1102–14. Rakowski, W., Ehrich, B., Goldstein, M., Rimer, B., Pearlman, D., Clark, M., Valicer, W. and Woolverton, H. (1998). ‘Increasing mammography screening among women aged 40–74 by use of a stage-matched, tailored intervention.’ Preventative Medicine, 27, 748–56. Velicer, W., DiClemente, C., Prochanska, J. and Brandenburg, N. (1985). ‘Distribution of smokers by stage in three representative samples.’ Preventative Medicine, 24, 401–11. Wheatley, K. (1997). ‘An overview of issues in work-related driving.’ In Staysafe 36: Drivers as Workers, Vehicles as Workplaces: Issues in Fleet Management. (Report No. 9/51). Ninth report of the Joint Standing Committee on Road Safety of the 51st Parliament. Sydney: Parliament of New South Wales, 15–24. Whysall, Z., Haslam, C. and Haslam, R. (2006). ‘Implementing health and safety interventions in the workplace: an exploratory study.’ International Journal of Industrial Ergonomics, 36, 809–18. World Health Organization (2004). ‘Road safety: a public health issue.’ www.who. int/features/2004/road_safety/en/print.html.
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Chapter 14
Prospective Relationships between Physical Activity, ‘Need for Recovery’ and Driver Accidents and Absenteeism Adrian Taylor1 and Lisa Dorn2 1 University of Exeter, UK 2 Cranfield University, UK Introduction It is recognised that driver stress and fatigue are major contributors to at-work road traffic accidents (Matthews and Desmond, 1998) and occupational health (Sluiter et al., 2003). However, it is less clear, and has been of little concern, if and how (increasingly) low levels of physical activity in western countries (Department of Health, 2004), are influencing driver stress, fatigue, health status (absenteeism) and risk of driver accident (Shephard, 1996). Taylor and Dorn (2003; 2006) provided the first model and literature review to guide understanding of how physical (in)activity may influence risk of driver accidents. They proposed, through a number of possible pathways, that being regularly physically active may favourably influence psychological responses to stress by increasing resilience and energy, decreasing levels of fatigue and enhance sleep quality and health status. In turn these mediating factors could all contribute towards a lower risk of accidents and absenteeism. In contrast, a sedentary lifestyle may contribute to progression towards greater fatigue and less resilience for coping with the psychological demands of driving for work. Stress has been shown to reduce physical activity (Ng and Jeffery, 2003; Payne et al., 2002; Steptoe et al., 1996; Stetson et al., 1997), possibly due to a perceived need to recover from mental fatigue by resting after work. With decreasing physical activity, a downward spiral of increasing occupational fatigue (due to physical deconditioning) and stress may occur with increasing risk for driver accidents. Thus, the concept of ‘need for recovery’ would seem likely to be determined, to some degree, by general levels of physical activity, but also to have an impact on accidents and health status. In a unique study, Sluiter, van der Beek and Frings-Dresen (1997) reported that not only did less physically active coach drivers report a longer ‘need for recovery’ after work, but were more likely to have an accident. Drivers engaging in more than one weekly session of exercise had 0.78 accidents per driver, whereas less-active drivers had 1.05 accidents over a two year period. The measures of physical activity used in the study were limited as this was not a main element of the Dutch study of
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long-distance bus drivers. Further research is therefore needed to better understand the link between physical activity, fatigue, health status, accidents and absenteeism. The purpose of the present study was to investigate if level of activity among trainee UK bus drivers is associated with future accidents and absenteeism, using a validated measure of physical activity. A secondary purpose was to investigate if self-reported fatigue (‘need for recovery’) mediates the relationship between physical activity and both accidents and absenteeism. Methods Participants A total of 183 newly recruited bus drivers were assessed over a one year period, as part of a larger study. Recruits attended a screening session at a large bus depot in the south of England. Each driver completed a battery of pencil and paper or computerbased tests. They also undertook a detailed assessment of their performance on a bus driving simulator (reported elsewhere). Measures Self-reported physical activity was assessed using the short selfadministered version of the International Physical Activity Questionnaire (IPAQ: Craig et al., 2003). This seeks to gain information on the number of days and minutes per day in the previous seven days that the person engaged in vigorous and moderate intensity physical activity. Walking was a separate category, as was time spent sitting. The 11 item ‘need for recovery’ scale (Sluiter et al., 2003) was used to assess occupational-related fatigue. The scale includes items such as, ‘at the end of a working day I am usually feeling worn out’, ‘after a working day I am often too tired to start other activities’, with responses required on a five point scale from ‘strongly disagree’ (1) to ‘strongly agree’ (5). Self-reported health status was determined from the General Health Questionaire-12 (GHQ-12; Goldberg et al., 1997). A single item ‘Do you feel like you would like to do more physical activity than you currently do’ (with a yes/no response format), and other questions about perceived availability and preference for exercise opportunities were also asked. The number of at-fault, partly at-fault, and not at-fault bus accidents, over a subsequent three month period, were retrieved from a corporate database. Data analysis Data from the IPAQ was cleaned and scored (see guidelines at http:// www.ipaq.ki.se/), and the sample was categorised into three levels (as recommended following extensive international development work on the survey): low active (that is, < 600 MET-minutes/week of activity); moderately active (that is, 600–3000 MET-minutes/week of activity); and high active (that is, >3000 MET-minutes/week of activity). As recommended, subjects with scores of greater than 960 minutes per week of total vigorous, moderate and walking activity were excluded as outliers. Complete data (including the surveys and information on accidents and absenteeism) from 123 drivers are reported. Scores were derived for ‘need for recovery’ and GHQ12 by adding the respective items (following reverse coding as appropriate).
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Amount of physical activity was reported for each of the three levels of activity. Analyses of variance (ANOVA) was used to compare number of accidents, absenteeism, general health (GHQ-12), and ‘need for recovery’ for the three levels of physical activity. Given the unequal distribution of the sample across the three levels of physical activity and the number of outliers excluded (that is, possible over reporting of physical activity) using the IPAQ we also conducted a series of t-tests to compare those responding ‘yes’ and ‘no’ to the question about wishing to do more physical activity than the participants were engaging in currently. Regression analysis was performed to determine the effects of physical activity on the number of accidents and absenteeism directly, and indirectly, through ‘need for recovery’. Results The sample characteristics were as follows: mean age of 37.8 (SD 10.1)(ranging from 19–60 years), 85 per cent male, 2.2 years in formal post-16 years of age education, self-reported weight/height (body mass index) of 27.3 (SD 6.5), mainly white (79 per cent white, 8 per cent black or black British, 7 per cent Asian or Asian British), mainly married (44 per cent married, 21 per cent single, 17 per cent live with partner, 9 per cent separated or divorced), and 75 per cent had smoked, with 47 per cent currently smoking at the time of the study. The latter reported smoking a mean of 104 (SD 42) cigarettes a week (calculated from per weekday and weekend day). Table 14.1 shows the descriptive data for minutes per week of vigorous and moderate intensity exercise and walking for the three activity categories. It also shows the relationship between the categorised levels of physical activity and other variables of interest. There were no significant difference in accident rates and absenteeism over three months between the different levels of physical activity, although a trend did emerge. Low physically active drivers had approximately twice as many accidents and took almost six times as many days off work compared with high active drivers. Albeit with relatively small numbers, 8/17 (47 per cent), 22/44 (33 per cent) and 6/23 (26 per cent) of those in the low, moderate and high active groups had at least one accident, although a chi2 test failed to reveal a significant overall effect. Surprisingly, physical activity was not related to scores on the GHQ12. In contrast, level of physical activity was associated with ‘need for recovery’, with post hoc tests revealing that the high active group had significantly lower scores (expressed as differences and confidence intervals) than both the moderate (3.6; 0.1 to 7.1) and low (6.0; 0.96 to 11.1) active drivers.
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Table 14.1
The relationship between three levels of physical activity and ‘need for recovery’, health status, accidents and absenteeism Low active
Vigorous minutes/week Moderate mins/week Walking Total METmins/week Total accidents after three months Absent days after three months GHQ-12 ‘Need for recovery’
Mean (SEM) N =15 5.88 (18.49)
Moderate active Mean (SEM) N = 76 73.22 (8.52)
High active Mean (SEM) N = 28 235.00 (14.40)
10.00 (28.92)
97.44 (13.33)
241.61 (22.53)
84.12 (45.47)
293.71 (20.96)
275.00 (35.43)
364.62 (139.34) 0.76 (0.20)
1935 (64.24) 0.53 (0.10)
3753.93 (108.58) 0.35 (0.17)
1.23 (0.37)
0.63 (0.17)
0.21 (0.28)
2.2 (2,122) p = 0.09
34.38 (1.20) 29.80 (1.68)
34.09 (0.55) 27.41 (0.75)
34.12 (0.96) 23.79 (1.23)
0.0 (2,113) ns 4.9 (2,116) p = 0.009
F (df) 61.8 (2,122) p < 0.001 23.21 (2,122) p < 0.001 8.9 (2,122) p < 0.001 196.7 (2,122) p < 0.001 1.2 (2,103) ns
Figure 14.1 shows a path model for the direct and indirect effects of estimated energy expenditure (from multiplying vigorous, moderate and walking mins by 8.0, 4.0 and 3.3 METs or units of energy expenditure, respectively; 1 is assumed to be equivalent to resting) from the sum of all forms of physical activity (from the IPAQ) in the past week at baseline, on accidents and absenteeism over the next three months. Confirming data in Table 14.1, physical activity was not significantly associated with either accidents or absenteeism. Physical activity was associated with ‘need for recovery’, which in turn predicted the number of accidents over three months. Mean (SEM) ‘need for recovery’ scores for those who had non versus at least one accident were 25.4 (0.7) and 28.7 (1.4)(t136 = 2.3, p < 0.05), respectively, but there were no differences in physical activity. ‘Need for recovery’ did not predict absenteeism. A series of paired t-tests revealed that those who would like to be more physically active (n = 126)(compared with those who didn’t, n = 42) reported a significantly (corrected for unequal variances) greater mean (SEM) ‘need for recovery’, t77 = 4.0, 27.4 (0.6) v 22.7 (0.9), p < 0.001; more accidents, t93 = 2.3, 0.53 (0.08) v 0.26 (0.09), p < 0.05; and more days absent, t159 = 1.9, 0.93 (0.21) v 0.43 (0.15), p = 0.05.
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Physical activity
-.04
-.24 **
0.08
Need for recovery .21 * Accidents in 3 months
.06 Absenteeism in 3 months
Figure 14.1 The mediating effect of ‘need for recovery’ between physical activity (total MET-mins/week) and accidents and absenteeism (after three months) Discussion The absence of a significant difference in accident and absenteeism rates between low and high physical activity categories did not appear to be due to lack of statistical power. However, further analyses, in the on-going study, will be conducted as the number of accidents and days absent from work accumulates post-study over six and twelve months into employment. The idea that regular physical activity provides protection against accidents through greater energy, faster recovery and less workrelated fatigue received some support from the present findings. The need for recovery appears to be an important psychological variable and indicator of work-related stress and fatigue, as Sluiter and colleagues (2003) have previously reported. The study also supports previous findings that physical activity is related to ‘need for recovery’ (Sluiter et al. 1997). The prevalence of physical activity was unusually high for the present sample of professional drivers compared with other studies (Taylor and Dorn, 2006). However, the likely over-reporting by the participants did not limit the comparison between levels of activity. The use of a proxy measure of activity (that is, preference to do more activity; yes or no) helped to provide an additional insight into understanding the findings. Approximately 75 per cent of the sample expressed a desire to do more activity, and these drivers also reported much higher levels on the ‘need for recovery’ scale. They also had more accidents and absenteeism. Regular physical activity can offer greater coping resources and reduce fatigue (Puetz, O’Connor and Dishman, 2006) and single sessions can reduce cardiovascular stress reactivity to mental challenges (Hamer, Taylor and Steptoe, 2005). It would therefore seem an important behaviour to promote in the context of at work driver health. The challenge is that with occupational induced fatigue, the need to recover through physical inactivity (for example, watching tv) may be a common cognition.
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The effect of mood on behaviour is of increasing interest, with a need to promote moderate physical activity as something that is pleasurable and activating. Overall, the drivers in the study reported being more active than expected, with almost 90 per cent reporting that they achieved the Department of Health’s guidelines of at least 150 minutes of moderate or vigorous physical activity per week. We excluded data from 46 drivers in line with IPAQ guidelines for removing outliers, and it would seem likely that there was an overall inflation of physical activity reported using this survey. A simple question about desire to be more active revealed that 81 per cent would, with 91 per cent in the low active group indicating that they would like to do more physical activity. Interestingly, overall 78 per cent and 52 per cent of the sample, respectively, indicated that they would use a gym at least twice a week if one were available at the workplace, and it was free or if there was a charge of £3 per visit. Conclusions In summary, this study provides some preliminary evidence that promoting physical activity may be an important factor to consider for reducing at-work crash risk and absenteeism. It is also interesting to note that the sample of bus drivers were interested in being more physical active. It is only the second study to show a link between physically activity and risk of at work crashes, albeit a small one, with a relatively small study. ‘Need for recovery’ is further supported as an important construct that mediates the link between physical activity and accidents among professional drivers. References Craig, C.L., Marshall, A.L., Sjostrom, M., et al. (2003). ‘International physical activity questionnaire: 12-country reliability and validity.’ Medicine and Science in Sports and Exercise, vol. 35, 1381–95. de Croon, E.M., Sluiter, J.K. and Frings-Dresen, M.H. (2003). ‘Need for recovery after work predicts sickness absence: a two year prospective cohort study in truck drivers.’ Journal of Psychosomatic Research, vol. 55, 331–9. Department of Health (2004). At Least Five a Week: Evidence on the Impact of Physical Activity and its Relationship to Health. London: Dept of Health. Ekkekakis, P., Hall, E.E., VanLanduyt, L.M. and Petruzzello, S.J. (2000). ‘Walking in (affective) circles: can short walks enhance affect?’ Journal of Behavioral Medicine, vol. 23, 245–75. Goldberg, D.P., Gater, R., Sartorius, N., Ustun, T.B., Piccinelli, M., Gureje, O. and Rutter, C. (1997). ‘The validity of two versions of the GHQ in the WHO study of mental illness in general health care.’ Psychological Medicine, vol. 27, 191–7. Hamer, M., Taylor, A.H. and Steptoe, A. (2006). ‘The effect of acute aerobic exercise on blood pressure reactivity to psychological stress: a systematic review’, Biological Psychology, vol. 71, 183–90. Marcus, B.H., Rossi, J.S., Selby, V.C., Niaura, R.S. and Abrams, D.B. (1992). ‘The
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stages and processes of exercise adoption and maintenance in a worksite sample.’ Health Psychology, vol. 11, 386–95. Puetz, T.W., O’Connor, P.J. and Dishman, R.K. (2006). ‘Effects of chronic exercise on feelings of energy and fatigue: a quantitative synthesis.’ Psychological Bulletin, vol. 132, 866–76. Shephard, R.J. (1996). ‘Worksite fitness and exercise programs: a review of methodology and health impact.’ American Journal of Health Promotion, vol. 10, 436–52. Sluiter, J.K., van der Beek, A.J. and Frings-Dresen, M.H. (1997). ‘Workload of coach drivers. [Werkbelasting touringcarchauffeurs].’ Rep. No. 97-03, Amsterdam: Coronel Inst. Occup. Environ. Health, Acad. Medical Centre, 1–71. Sluiter, J.K., de Croon, E.M., Meijman, T.F. and Frings-Dresen, M.H.W. (2003). ‘Need for recovery from work-related fatigue and its role in the development and prediction of subjective health complaints.’ Occupational and Environmental Medicine, vol. 60, 62–70. Taylor, A.H. and Dorn, L. (2003). ‘The effects of exercise on stress, fatigue, sleep, health status and potential risk of at-work road traffic accidents: a multi-disciplinary model.’ In L. Dorn (ed.). Proceedings of 1st International Conference on Driver Behaviour and Training, Stratford-upon-Avon, UK: Ashgate Publishers, 175–90. Taylor, A.H. and Dorn, L. (2006). ‘Effects of physical inactivity on stress, fatigue, health and risk of road traffic accidents.’ Annual Review of Public Health, vol. 27, 371–91.
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Chapter 15
Predicting High Risk Behaviours in a Fleet Setting: Implications and Difficulties Utilising Behaviour Measurement Tools Jeremy Davey, James Freeman and Darren Wishart Centre for Accident Research and Road Safety, Queensland (CARRS-Q), Australia Introduction Fleet and work-related motor vehicle crashes represent a substantial physical, emotional and financial cost to the community. Previous estimations have indicated that the total cost of work-related road incidents in Australia was in the vicinity of $1.5 billion (Wheatley, 1997). More recent evidence has suggested that the average total insurance cost of a fleet incident to organisations and society is approximately $28 000 (Davey and Banks, 2005), while the average cost of a fatal crash in the general Australian motoring community is estimated to be $2 million (Austroads, 2006). Furthermore, estimates of the true cost for work-related crashes suggest that hidden costs may be somewhere between 8–36 times vehicle repair/replacement costs (Murray et al., 2003). Of note is that a high proportion of work-related deaths and injuries within the overall road toll consist of work-related crashes (Murray et al., 2003; Wheatley, 1997), as work-related traffic injuries have been estimated to be twice as likely to result in death or permanent disability than other workplace accidents (Wheatley, 1997). Driving assessment tools Given the tremendous burden that road crashes have on society, researchers are directing their focus towards investigating the attitudes and behaviours of general motorists, as well as determining the value of such self-reported data to predict crash involvement. Such measurement tools include: the Driving Skill Inventory (Lajunen and Summala, 1997), Driver Anger Scale (Deffenbacher, Oetting and Lynch, 1994), the Manchester Driver Behaviour Questionnaire (DBQ) (Reason et al., 1990), Driver Attitude Questionnaire (DAQ) (Parker et al., 1996) and the Safety Climate Questionnaire-MD (SCQ-MD) (Glendon and Litherland, 2001). The latter three questionnaires are proving increasingly popular in identifying the factors associated with vehicle crashes and demerit point loss among fleet drivers in work settings, and will remain the focus of the present study.
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Firstly, in regards to the DBQ, this measurement tool has been extensively utilised within a range of driver safety research areas such as: the genetics of driving behaviour (Bianchi and Summala, 2004), age differences in driving behaviour (Dobson et al., 1999), cross cultural studies (Lajunen et al., 2003) and associations with the likelihood of being involved in an accident (Dobson et al., 1999; Parker et al., 1995; Reason et al., 1990). Such research has predominantly focused on general motorists, which has indicated that speeding violations are one of the most common factors associated with crash involvement (Parker et al., 1995). Another driving tool which is beginning to receive increasing attention within the road safety literature is the Driver Attitude Questionnaire (Parker et al., 1996). Research has begun to utilise the DAQ within a number of different applied settings such as: speed awareness training (Meadows, 2002), general driver training programs (Burgess and Webley, 2000), bicycle interventions (Anderson and Summala, 2004), as well as fleet programmes (Davey et al., 2006; Wishart et al., 2006). Preliminary research indicates that the DAQ has the potential to be utilised to investigate motorists’ attitudes towards key road safety issues, such as drink driving, risky overtaking, close following and driving above the speed limit, with motorists generally reporting the most lenient attitudes towards speeding violations (Davey et al., 2006; Meadows, 2002; Wishart et al., 2006). The Safety Climate Questionnaire-Modified for Drivers (SCQ-MD) is also being utilised within road safety arenas, as researchers begin to recognise the importance of an organisation’s attitudes towards fleet and road safety issues. In simple terms, ‘climate’ relates to how employees perceive the organisational culture and practice of a company (Glendon and Stanton, 2000), and it is hypothesised that this perception impacts upon the way in which workers ultimately behave at work (Wills, 2006). In regards to safety climate, a growing body of research is demonstrating a link between safety culture and a variety of outcomes, ranging from vehicle crash rates (Diaz and Cabrera, 1997; Mearns, Whitaker and Flin, 2003) to injury severity (Gillen, Baltz, Gassel, Kirsch and Vaccaro, 2002). For example, Wills, Watson and Biggs (forthcoming) investigated the driving behaviours of 323 fleet employees and reported that work pressure and communication were significantly related to driver distraction. Also, Newnam, Watson and Murray (2002) examined the self-reported driving behaviours of fleet drivers and reported that the safety policies and practices within organisations had a direct impact on driving performance. Taken together, research is beginning to suggest that perceptions regarding the safety policies and practices of organisations may have a direct impact on driving outcomes. Fleets However, despite the prevalence of research currently focusing on identifying the self-reported attitudes and behaviours that influence crash involvement, relatively little research has endeavoured to examine the self-reported driving behaviours of those who drive company sponsored vehicles and/or spend long periods of time behind the wheel (Newnam et al., 2002; Newnam et al., 2004; Sullman et al., 2002; Xie and Parker, 2002). The lack of assessment tools in the Australian context appears to be a critical oversight as changes in industry/employer accountability, Occupational
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Health and Safety (OHS) legislation, Workers Compensation legislation and public liability are requiring industry to develop better benchmarking along with more comprehensive intervention programmes related to fleet safety. Currently, fleet organisations cannot effectively assess current risk and thus also cannot target and develop interventions nor evaluate the effectiveness of countermeasures due to the lack of adequate measurement tools. As a result, fleet companies are experiencing difficulties meeting their legislative requirements to reduce risk (OHS) despite compulsory third party insurance companies demanding increasingly better fleet safety environments across organisations. What is presumed is that drivers of employer owned vehicles who drive for work-related purposes generally engage in a higher prevalence of aberrant driving behaviours such as speeding (Stradling, 2000), and are at greater risk of crash involvement due to their exposure to the driving environment (Newnam et al., 2002; Sullman et al., 2002). Preliminary evidence suggests that speeding is the most likely illegal behaviour to be reported by fleet drivers (Davey et al., 2007; Dimmer and Parker, 1999; Wishart et al., 2006). However, further research appears necessary to determine which self-reported measurement tools are most useful within fleet settings as well as what specific attitudinal and behavioural factors predict crash involvement within such settings. As a result, the present research aimed to utilise three prominent driving measurement tools to investigate the relationship between self-reported attitudes, behaviours and crash involvement. More specifically, the study aimed to: 1. examine a group of fleet drivers’ attitudes and behaviours regarding road safety issues via three measurement tools (i.e., DBQ, DAQ and SCQ-MD); 2. investigate the relationship the sub-factors of the measurement tools have with self-reported crash involvement; and 3. report on the associated difficulties utilising behaviour measurement tools in fleet settings. Method Participants A total of 4195 individuals from a large Australian company volunteered to participate in the study. There were 3642 males (88.9 per cent) and 553 females (11.1 per cent). The average age of the sample was 43.7 years (range 18–66 years). Participants were located throughout Australia in both urban and rural areas. The sample consisted of approximately equal numbers of office workers, n = 2244 (46.8 per cent) and field workers, n = 2264 (47.2 per cent), with n = 284 (5.9 per cent) of respondents not indicating their employment type. Examination of vehicle types revealed that the largest proportion of the sample reported driving sedans (n = 2872, 61.2 per cent), followed by station wagons (n = 1375, 28.69 per cent), vans (n = 861, 18 per cent) and ‘customer service vehicles’ (CSV) (n = 518, 11 per cent), with only a small
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percentage indicating usage of four-wheel drive vehicles,1 utes or heavy vehicles. The majority of driving by participants was reported to be within the city n = 1988 (42.4 per cent), or in the city and on country roads n = 1867 (39.82 per cent), with only 767 participants (16.36 per cent) reporting driving on rural roads. On average participants had held their licence for 26 years. A total of 588 participants reported being involved in a crash while driving for work in the past 12 months. Questionnaire Driver Behaviour Questionnaire (DBQ) In the current study a, modified version of the DBQ was used, which consisted of 20 items. Questions relating to lapses were omitted due to previous research indicating that this factor is not associated with crash involvement (Lawnton et al., 1997). In addition, the authors of the current paper made minor rewording or rephrasing modifications, in order to make the questionnaire more representative of Australian driving conditions. For example, references to turning ‘right’ were removed on some items, as there are instances where drivers may attempt to overtake someone who is turning left.2 Respondents were required to indicate on a six point scale (0 = ‘never’ to 5 = ‘nearly all the time’) how often they commit each of the errors (eight items), Highway Code violations (eight items) and aggressive violations (four items). Driver Attitude Questionnaire (DAQ) The DAQ is a 20 item self-report questionnaire designed to measure attitudes regarding a range of driving behaviours, which are collated to identify four factors: drink driving, close following, dangerous overtaking and speeding. Respondents are required to indicate on a six point Likert scale (0 = ‘strongly disagree’ to 5 = ‘strongly agree’) their agreement with statements regarding the appropriateness of various driving behaviours. Safety Climate Questionnaire (SCQ) A 29 item version of the SCQ was utilised in the research project. Minor modifications ensured that the questions related specifically to ‘work-related driving’. The SCQ contains five sub-factors that aim to measure perceptions towards fleet safety rules, communication and support, work pressures, adequacy of fleet safety procedure and management commitment. A growing body of research has demonstrated that the SCQ is a reliable tool to measure fleet drivers’ attitudes towards the safety climate of an organisation (Wills et al., 2006; Wills et al., forthcoming).
1 Other than the CSV body type. 2 Previous research has demonstrated that the DBQ is robust to minor changes to some items in order to reflect specific cultural and environmental contexts (Blockey and Hartley, 1995; Ozkan and Lajunen, 2005; Parker et al., 2000).
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Demographic measures A number of socio-demographic questions were included in the questionnaire to determine participants’ age, gender, driving history (for example, years experience, number of traffic offences and crashes) and their weekly driving exposure (for example, type of car driven and driving hours). Procedure The vehicle insurance company provided a list of individuals who expressed interest in participating in the research. A letter of introduction, the study questionnaire and a reply paid envelope were distributed through the company’s internal mail system to the participants. In total, 1440 were mailed out and 443 were returned indicating a 30 per cent response rate. Results Structure and reliability of the questionnaires for an Australian sample The internal consistency of the DBQ, DAQ and SCQ-MD scores were examined through calculating Cronbach’s alpha reliability coefficients, and are presented in Table 15.1. The SCQ factors, which specifically relate to safety, appear to exhibit the highest level of internal consistency. Similar to previous Australian research (Blockey and Hartley, 1995; Dobson et al., 1999), and on professional drivers (Sullman et al., 2002), the DBQ factors also appear to exhibit relative internal consistency. In contrast, there has been little research to determine the psychometric properties of the DAQ, and although only moderate, the alpha coefficients are similar to previous research (Meadows, 2002). Table 15.1 also displays the overall mean scores for the DBQ, DAQ and SCQMD factors. Higher means on the DBQ indicate more deviant driving behaviours, while higher scores on the DAQ and SCQ-MD indicate more appropriate road safety attitudes, and positive perceptions regarding the organisation’s road safety culture, respectively. Firstly, an examination of the mean scores reveals that for the DBQ scale, participants were most likely to engage in speeding offences while at work, which was significantly more likely compared with committing driving errors F(1, 4195) = 70.73, p . af Wåhlberg, A. (2003). ‘Stability and correlates of driver acceleration behaviour.’ In L. Dorn (ed.), Driver Behaviour and Training. First International Conference on Driver Behaviour and Training. Stratford-upon-Avon, 11–12 November, 2003, 45–54. af Wåhlberg, A. (2004). ‘The stability of driver acceleration behaviour and a replication of its relation to bus accidents.’ Accident Analysis and Prevention, 36, 83–92. af Wåhlberg, A. (2006a). The Prediction of Traffic Accident Involvement From Driving Behaviour. Doctoral thesis, Uppsala University, Sweden. af Wåhlberg, A. (2006b). ‘Short-term effects of training in economical driving; passenger comfort and driver acceleration behaviour.’ International Journal of Industrial Ergonomics 36, 151–63. af Wåhlberg, A. (2006c). ‘Speed choice versus celeration behaviour as traffic accident predictor.’ Journal of Safety Research, 37, 43–51. af Wåhlberg, A. (2006d), ‘Driver celeration behaviour and the prediction of traffic accidents.’ International Journal of Occupational Safety and Ergonomics, 12, 281–96. af Wåhlberg, A. (2007a), ‘Aggregation of driver celeration behaviour data: effects on stability and accident prediction.’ Safety Science, 45, 487–500. af Wåhlberg, A. (2007b), ‘Effects of passengers on bus driver celeration behaviour and incident prediction.’ Journal of Safety Research, 38, 9–15. af Wåhlberg, A. (forthcoming). ‘Long term effects of training in economical driving: fuel consumption, accidents, driver acceleration behaviour and technical feedback.’ International Journal of Industrial Ergonomics. af Wåhlberg, A. (submitted). ‘Driver celeration behaviour and accidents – an analysis.’ www.psyk.uu.se/hemsidor/busdriver.
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Appendix: Measurement method The speed signal is tapped from the vehicle’s speedometer with a frequency of 10Hz, an interval of 100 ms. During this interval, the number of complete puls-cycles is counted, and speed and acceleration are calculated with the formulas shown in Figure 16.1. Example
Puls 0
Cycletime T0
Puls 1
Cycletime T1
Puls 2
Cycletime T2
Measurement interval 100ms
Figure 16.1 Measurement method example Example t = T0 + T1 + T2 n=3 Speed calculation formula v = ((n / t)/w)*1000 where v = speed (m/s) n = number of pulses from the speedometer t = time for n pulses to accumulate (seconds) w = pulses per kilometre Acceleration calculation formula a(4n) = ((v(4n) + v(4n+1) + v(4n+2) + v(4n+3)) / 4 – (v(4n) + v(4n–1) + v(4n–2) + v(4n–3))/4) / 2.5 where: a = acceleration (m/s2) v = speed (m/s) n = number of acceleration measurement points
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Chapter 17
A Study of Contemporary Modifications to the Manchester Driver Behaviour Questionnaire for Organisational Fleet Settings James Freeman, Jeremy Davey and Darren Wishart Centre for Accident Research and Road Safety, Queensland (CARRS-Q), Australia Introduction DBQ and the present driving context The Manchester Driver Behaviour Questionnaire (DBQ) is increasingly becoming one of the most prominent measurement scales to examine self-reported driving behaviours (Lajunen and Summala, 2003). For example, the DBQ has been extensively utilised in a range of driver safety research areas, such as: age differences in driving behaviour (Dobson et al., 1999), the genetics of driving behaviour (Bianchi and Summala, 2004), cross cultural studies (Lajunen et al., 2003), as well as factors contributing to accident involvement (Dobson et al., 1999; Parker et al., 1995b) and demerit point loss (Davey et al., 2007). Furthermore, the versatility of the DBQ has also been demonstrated via the utilisation of the instrument in a number of countries, including China (Xie and Parker, 200), Australia (Davey et al., 2006; Dobson et al., 1999; Newnam, Watson and Murray, 2004), New Zealand (Sullman, Meadows and Pajo, 2002), Finland (Bianchi and Summala, 2004) and the United Kingdom (Parker et al., 1995a; Parker et al., 2000). The popularity of the DBQ to assess current driving performance is also reflected in the considerable evolution of the scale since its inception. The original DBQ was developed by Reason et al. (1990) and focused on two distinct driving behaviours that were identified as errors and violations. Errors were believed to consist of actions that are not planned (for example, mistakes and misjudgements), while violations were considered to be deliberate deviations from safe driving behaviours (for example, speeding). However, an additional factor referred to as ‘slips and lapses’ was also developed that focused on attention and memory failures, which were traditionally not considered to affect overall road safety. Specifically, such behaviours were associated with attention and memory problems, while errors include more serious mistakes such as failures of observation and misjudgement (Lajunen and Summala, 2003). The original DBQ scale has undergone further modification by Lawton et al. (1997), incorporating additional items to assess other factors that have been proposed
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to contribute to driving violations. For example, aggressive violation items have been included in the questionnaire and focus on an interpersonal aggressive component, such as showing or exhibiting frustration. However, ‘ordinary’ violations remained within the scale and consist of aberrant driving behaviours that do not have an aggressive aim, for example, speeding behaviours. Taken together, currently the scale distinguishes between two forms of violations: Highway Code violations (for example, speeding and running red lights) and interpersonal aggressive violations (for example, chasing another motorist when angry and sounding one’s horn). A closer examination of the definitions reveal that Highway Code violations focus on gaining an advantage such as speeding and engaging in risky overtaking manoeuvres, while aggressive violations are more hostile in nature and are usually directed towards other motorists. In addition to the considerable level of modification of DBQ items, there has been a high level of variation within the literature regarding the number of factors identified from using the DBQ. Firstly, some earlier research confirmed the original three factors of errors, violations and lapses (Adberg and Rimmo, 1998; Blockey and Hartley, 1995; Parker et al., 1995a). For example, Aberg and Rimmo (1998) identified inattention and inexperience error factors from a large group of Swedish drivers, but overall found the same factor structure. In contrast, there has been evidence of four factors reported by Sullman et al. (2002) that focused on errors, lapses, aggressive violations and ordinary violations. Similarly, Lajunen et al. (2003) identified four factors with a group of UK drivers, and Mesken et al. (2002) reported four factors (errors, lapses, speeding and interpersonal violations) when examining the driving behaviours of Finnish motorists. In addition to the different number of factors identified, research has generally reported differences in factor structure, as specific items often load on different factors depending on the driving context (Davey et al., 2006), which ultimately influences the naming and interpretation of each factor. Despite such variability, previous applied research has demonstrated that the DBQ is robust to minor changes to some items that have been made to suit specific cultural and environmental contexts (Blockey and Hartley, 1995; Davey et al., 2007; Ozkan and Lajunen, 2005; Parker et al., 2000). As highlighted above, the DBQ has been utilised in a number of motorised countries and has thus been translated and modified to tailor a vast array of driving situations. Professional drivers and fleet safety Despite the tremendous amount of research that has utilised the DBQ to investigate general motorists’ driving behaviours, there is currently only a small (but expanding) body of knowledge regarding the self-reported driving behaviours of those who drive on public roads for professional reasons (Davey et al., 2006; Newnam et al., 2002; Newnam et al., 2004; Sullman et al., 2002; Xie and Parker, 2002). That is, relatively little research has endeavoured to examine the driving behaviours of those who drive company sponsored vehicles and/or spend long periods of time behind the wheel (Davey et al., 2006; Newnam et al., 2004), despite this group being at a greater level of risk to accident involvement (Newnam et al., 2002; Sullman et al., 2002), either through increased exposure to the road or as a result of time pressures
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and other distractions (Stradling et al., 2000). This lack of research is of particular concern as early estimates suggest work related road incidents cost approximately AUD$1.5 billion (Wheatley, 1997), with the hidden costs somewhere between 3 and 36 times vehicle repair/replacement costs (Murray et al., 2003). Similar to the above, the small amount of fleet-based research that has utilised the DBQ has also reported a high level of factor structure variability for the measurement tool. For example, Xie and Parker (2002) examined the driving behaviours of professional drivers and identified four factors for example, violations, lapses and errors. In contrast, Sullman et al. (2002) utilised the DBQ to examine factors associated with crash involvement and reported four factors, while Dimmer and Parker (1999) focused on company car drivers and reported a six factor DBQ structure. One of the few Australian studies by Davey et al. (2006) utilised the DBQ to examine the behaviours of a group of fleet drivers and reported a traditional three factor solution of errors, aggressive and speeding violations, although it is noted that a greater number of traditional items considered to be speeding violations actually loaded on the aggressive violation factor. That is, the aggressive violations factor consisted of a mixture of emotion-oriented responses to driving situations and traditional Highway Code violations. Contemporary DBQ modifications When considering that the work vehicle may be increasingly becoming an extension of the office (for example, taking phone calls), the process of multitasking while driving and time pressures placed on drivers may yet prove to have a direct impact on driving performance (Davey et al., 2006). As a result, there appears to be an opportunity to identify additional contemporary factors that may influence professional driving performance, such as fatigue, time pressure and multitasking (for example, driving and eating and/or mobile phone use) and determine what impact, if any, such issues have on driving performance. Therefore, the present research aims to extend the traditional DBQ by including contemporary items to the traditional 20 item measurement tool and investigate the utility of the additional items in predicting aberrant driving behaviours. More specifically, the study endeavoured to: • •
examine the factor structure of the DBQ after inclusion of contemporary fleet driving items; and investigate the relationship the modified DBQ has with self-reported crash involvement and traffic offences.
Method Participants A total of 443 individuals, who were all employees of a large insurance company in Australia, volunteered to participate in the study. There were 345 (78 per cent) males and 98 (22 per cent) females. The average age of the sample was 44 years (range
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18–68 years). Participants were located throughout Australia in both urban and rural areas. The largest proportion of vehicles driven by participants were reported to be for tool of trade (56 per cent), although vehicles were also salary sacrificed (43 per cent), and a small proportion were leased or the participant’s own vehicle (1 per cent). Vehicles were reported to be sedans (85 per cent), four wheel drives (12 per cent) or other (3 per cent). The majority of driving by participants was reported to be within the city (46 per cent), or in the city and on country roads (40 per cent). On average participants had held their licence for 26 years (range 5–48 years), and had been driving a work vehicle for approximately five years (range 1–33 years), with the largest proportion driving between 11 and 20 hours per week (43 per cent), and between 30 000–40 000 km per year. Materials Driver Behaviour Questionnaire (DBQ) A modified version of the DBQ, consisting of 35 items was used. Similar to previous research, questions relating to lapses were omitted due to earlier evidence that has indicated this factor is not associated with crash involvement (Lawnton et al., 1997; Stradling, personal communication, 2003). In addition to the traditional 20 items incorporated with the DBQ, the authors of the current paper also included another 15 items that focused specifically on contemporary fleet safety issues such as fatigue, tiredness and multitasking. These items were derived from the implementation of focus groups with fleet drivers from a number of large Australian fleet organisations which facilitated the identification of key themes proposed to influence driving performance such as fatigue, tiredness, multitasking and general distraction. Some of the added items were ‘drive while under time pressure’, ‘eat a meal while driving for work’ and ‘drive while using a mobile phone’. Respondents were required to indicate on a six point scale (0 = ‘never’ to 5 = ‘nearly all the time’) how often they commit each of the errors (eight items), Highway Code violations (eight items), aggressive items (four items), as well as the additional 15 items. The complete list of the additional questions are: 1. Drive while tired 2. Have difficulty driving because of tiredness or fatigue 3. Find yourself nodding off while driving for work 4. Drive while under time pressure 5. Find yourself driving on autopilot 6. Save time during the day by driving quicker between jobs 7. Eat a meal while driving for work 8. Find your attention being distracted from the road 9. Drive home from work after a long day 10. Not wear your seatbelt
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11. Remove your seatbelt for some reason while driving 12. Do paperwork or other administration work while driving 13. Drive while using a ‘hand-held’ mobile phone 14. Drive while using a ‘hands-free’ mobile phone 15. Do paperwork or admin while driving Demographic measures A number of socio-demographic questions were included in the questionnaire to determine participants’ age, gender, driving history (for example, years experience, number of traffic offences and crashes) and their weekly driving exposure (for example, type of car driven, driving hours). Procedure The vehicle insurance company provided a list of individuals who expressed interest in participating in the research. A letter of introduction, the study questionnaire and a reply paid envelope was distributed through the company’s internal mail system to the participants. In total 1440 were mailed to fleet drivers and 443 were returned, which indicates a 30 per cent response rate. Results Factor structure and reliability of the Driver Behaviour Questionnaire for an Australian sample The internal consistency of the original DBQ factor scores was examined through calculating Cronbach’s alpha reliability coefficients, which are presented in Table 17.1. Similar to previous Australian research (Blockey and Hartley, 1995; Dobson et al., 1999) and research on professional drivers (Sullman et al., 2002), the factors appear to exhibit relative internal consistency. Examination of the scores reveals that the items traditionally associated with Highway Code violations indicate the highest reliability coefficients (0.80) while aggressive violations, which consisted of only four items, had the lowest reliability (0.60). Table 17.1 Alpha reliability coefficients of the DBQ scale
Errors (8 items) Highway Code violations (8 items) Aggressive violations (4 items)
Current
Sullman et al.
0.77 0.80 0.60
0.71 0.62 0.57
Sample (2002)
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Self-reported frequent driving behaviours Table 17.2 reports the overall mean scores for the three factors, revealing that participants reported a similar frequency for each of the driving categories, although further analyses indicated Highway Code violations occurred significantly more frequently than errors F(1, 443) = 80.73, p < 0.01 as well as aggressive violations F(1, 433) = 94.42, p < 0.01. The means are higher than previous research that has focused on college students (Bianchi and Summala, 2004) elderly drivers (Parker et al., 2000) and professional drivers (Sullman et al., 2002; Xie and Parker, 2002), indicating that the current sample engaged in, or at least reported, a higher level of aberrant driving behaviours.1 In addition, Table 17.2 reports the mean and standard deviation scores for the four highest ranked items from the complete 35 item questionnaire, which were: ‘drive while under time pressure’ (M = 2.79, SD = 1.20); ‘drive while tired’ (M = 2.68, SD = 1.10); ‘exceed the speed limit on a highway’ (M = 2.62, SD = 0.93) and ‘find your attention being distracted from the road’ (M = 2.26, SD = 0.83). The results indicate that while speeding remains one of the most common forms of aberrant behaviour reported by the fleet drivers (Newnam et al., 2004; Sullman et al., 2002), drivers are also at risk of driving while fatigued, tired or while distracted. Table 17.2 Mean scores for the DBQ factors
Errors (8 items) Highway Code violations (8 items) Aggressive violations (4 items) Highest Ranked Items 1. Drive while under time pressure 2. Drive while tired 3. Exceed the speed limit on a highway 4. Find your attention being distracted from the road
M 1.61 1.70 1.53
SD 0.37 0.58 0.48
2.79 2.68 2.62 2.26
1.20 1.10 0.93 0.83
Factor analysis was administered on the complete 35 item questionnaire. Principal components analysis with oblique rotation was implemented to determine the factor structure of the DBQ, which revealed a three factor solution that accounted for 49 per cent of the total variance. The first factor accounted for approximately 31 per cent of the total variance and contained nine items relating to a combination of Highway Code violations and some aggressive violations. Firstly, seven Highway Code violation items loaded on the factor, with the first five speeding items identified 1 However, it is noted that the DBQ questionnaire utilised in the current study most likely varies slightly on the wording of some items compared to previous DBQ research, which should be borne in mind when making comparisons with previous research.
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as the strongest contributors to the factor (for example, race away from traffic lights, drive especially close, speed on residential road and so on). Secondly, similar to previous research (Davey et al., 2006), some aggressive items also loaded on the speeding factor. However, this factor was labelled Highway Code violations as the predominant theme to collectively emerge from the items focuses on speeding behaviours. The second factor accounted for ten per cent of the total variance and contained nine of the new items that centred on a combination of fatigue and distraction issues, such as driving while tired, nodding off while driving and driving on autopilot. It is noted that some additional items that may be perceived as being associated with multitasking and time pressure were also evident within this factor, such as eating a meal while driving and saving time during the day by driving quicker between jobs. While it was originally anticipated that ‘fatigue’ and ‘multi-tasking’ items would be represented in distinct factors, this did not occur in the current sample of drivers. Rather at this stage, the factor was labelled ‘fatigue’ as the largest proportion of items relate to symptoms that result from this experience, although it is noted that closer analytic scrutiny could produce a different interpretation. Finally, the third factor accounted for approximately eight per cent of the overall variance and comprised of 11 items, the majority associated with traditional error items; for example, miss a stop or give way sign, nearly hit a car and skid while breaking. However it is also noted that one traditional aggressive item also loaded on the factor (for example, become angered by another driver and give chase) and three new items, two of which focused on non-seat belt wearing and one on multitasking. All items and factors for the modified DBQ are reported in Table 17.3. Reliability and intercorrelations of the modified Driver Behaviour Questionnaire The internal consistency of the modified DBQ scale scores were examined through calculating Cronbach’s alpha reliability coefficients. The resulting analysis indicated internal consistency of: 0.82 = speeding/aggression, 0.76 = fatigue, 0.69 = errors. In addition, bivariate analysis indicated that the strongest relationship was between speeding and errors (r = 0.58**), followed by fatigue and errors (r = 0.52**) and then fatigue and speeding (r = 0.50**). Interestingly, there was only a moderate bivariate relationship between fatigue and hours driven per week (r = 0.20**) or number of kilometres driver per year (r = 0.24**). Prediction of offences The final part of the study aimed to examine the utility of the modified Driver Behaviour Questionnaire to predict self-reported work crashes as well as demerit point loss. Due to the relatively small number of participants who reported a workrelated crash in the last 12 months (N = 48), it was not possible to implement regression analyses and thus the following analyses focus on predicting work-related driving infringements (N = 73). A logistic regression analysis was performed to examine the contributions of the three factors (for example, speeding/aggression, fatigue/tiredness and errors) as well as driving exposure (for example, kilometres
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Table 17.3 Factor structure of the modified DBQ Description
F1
Race away from traffic lights to beat car beside you Drive especially close to the car in front to signal drive faster Disregard speed limit on a residential road Disregard speed limit on a highway or freeway Stay in a closing lane and force your way into another Become angered by another driver and show anger Cross junction knowing the traffic lights have already turned Become impatient by slow driver and overtake on inside Sound your horn to indicate your annoyance at another driver
0.73 0.64 0.62 0.62 0.57 0.55 0.54 0.52 0.41
Drive while tired Have difficulty driving because of tiredness or fatigue Find yourself nodding off while driving for work Drive while under time pressure Find yourself driving on autopilot Save time during the day by driving quicker between jobs Eat a meal while driving for work Find your attention being distracted from the road Drive home from work after a long day Nearly hit a cyclist while turning Not wear your seatbelt Remove your seatbelt for some reason while driving Become angered by another driver and give chase Skid while breaking or cornering on a slippery road When overtaking, underestimate speed of oncoming vehicle Attempt to overtake someone you had not noticed turning Miss stop or give way sign Pull out of junction so far that you disrupt traffic Nearly hit another car while queuing to enter a main road Do paperwork or other administration work while driving Fail to notice pedestrian crossing in path
0.41
F2
F3
0.79 0.79 0.66 0.64 0.63 0.61 0.54 0.54 0.44 0.63 0.63 0.60 0.58 0.55 0.52 0.52 0.49 0.48 0.45 0.41 0.40
Note: Five questions did not load. They were: (1) Have one or two alcoholic drinks before driving for work; (2) Fail to check rear-view mirror; (3) Drive while using a ‘hands-free’ mobile phone; (4) Drive while using a ‘hand-held’ phone and (5) Do paperwork or admin while driving.
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driven each year and hours driving per week) to the prediction of self-reported infringements in the past 12 months. Table 17.4 depicts the variables in each model, the regression coefficients, as well as the Wald and odds ratio values. Self-reported number of kilometres driven each year and hours of driving per week were entered in the first step to examine, as well as control for, the influence of driving exposure before the inclusion of the DBQ factors. As expected, the number of kilometres driven per year was predictive of incurring demerit point loss (p = 0.000) as those who drive longer distances are at a greater risk. Table 17.4 Logistic regression Variables
B
SE
Wald
p
Odds ratio Exp (B)
95% CI Lower
Upper
Step 1 Hours per week –0.148 0.18 Km per year 0.36** 0.10 Model Chi-Square 16.65** (df = 2)
0.58 14.18
0.447 0.000
0.87 1.42
0.61 1.19
1.24 1.72
Step 2 Hours per week Km per year Speeding/agress Errors Tiredness/ fatigue
1.02 11.47 1.09 0.01 4.69
0.312 0.001 0.924 0.702 0.030
0.87 1.39 1.05 1.05 1.56
0.57 1.15 0.39 0.81 1.046
1.20 1.70 2.92 1.35 2.431
–0.19 0.34** 0.27 0.05 0.466
0.19 0.09 0.26 0.52 0.215
Model Chi-Square 25.49** (df = 5) Block Chi-Square 12.89** (df = 3)
Next, the three modified DBQ factors were entered in the model to assess whether the proposed behaviours improved the prediction of demerit point loss over and above exposure to driving (Step 2). The additional variables collectively were significant, with a chi-square statistic of X² (4, 3 = 443) = 10.89, p = 0.005, as was the fatigue variable. The model indicates that as participants become more tired and/or lose concentration, the corresponding likelihood of engaging in infringements that results in demerit point loss increases (p = 0.030). Several additional regression models were estimated to determine the sensitivity of the results. A test of the full model with all six predictors entered together, as well as the two models entered separately, confirmed the same significant predictors (for example, exposure and fatigue). Forward and backward stepwise regression identified the same predictors. Inclusion of gender, age and years driving experience did not increase the predictive value of the model.
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Discussion The chapter aims to explore possible contemporary changes to the DBQ for utilisation within organisational fleet settings. More specifically, the chapter reports on an attempt to add additional contemporary driving behaviour items (for example, fatigue and multitasking) to the traditional DBQ in order to increase the utility of the measurement tool to examine and predict aberrant driving behaviours. At present, the DBQ has become increasingly popular as a measurement tool to investigate motorists’ self-reported driving behaviours (Lajunen et al., 2003; Parker et al., 1995b) as well as determine what driving behaviours are directly related to increased risk of crash involvement (Parker et al., 1995). However, within the professional driving setting, only a small body of research has begun to examine the driving behaviours of professional drivers (Newnam et al., 2004; Willis et al., 2004) and there has been little examination of necessary measurement tools to accurately capture their driving experiences and perceptions. Firstly, reliability analysis of the original DBQ indicated coefficients that were relatively robust and similar to both the small amount of previous Australian research (Blockey and Hartley, 1995; Dobson et al., 1999) and recent fleet safety findings (Sullman et al., 2002). Encouragingly, despite the subtle alterations to the DBQ to reflect Australian driving conditions, the reliability of the scale appears acceptable. Secondly, examination of the overall mean scores for the original DBQ factors revealed similar scores between the constructs, although it appears that the current sample were most likely to engage in Highway Code violations. This finding is consistent with previous research that has indicated speeding to be the most frequently reported aberrant driving behaviour on public roads (Dimmer and Parker, 1999; Lajunen et al., 2003). Furthermore, given the time pressures often placed on professional drivers, it may not be surprising that speeding violations are the most common form of aberrant behaviour both exhibited and reported by fleet drivers. However, once the additional items were analysed that focused on fatigue, tiredness and multitasking, it became evident that participants in the current sample were most likely to report driving while under time pressure as well as driving while tired, followed by exceeding the speed limit on the highway. While only preliminary, the results indicate that although speeding remains one of the most common forms of aberrant behaviour reported by the fleet drivers (Newnam et al., 2004; Sullman et al., 2002), drivers are also at risk of driving while fatigued, tired or while distracted. A series of factor analytic techniques were implemented to assist with the interpretation of the scale scores. Both exploratory and oblique rotations produced five factor models. While some previous studies have reported five factor structures (Parker et al., 2000), the present factors were difficult to interpret, which resulted in a three factor solution being sought. This endeavour proved fruitful as three factors emerged that generally consisted of errors, highway violations/aggressive violations and the fatigue/tiredness factor. The three factor model was relatively consistent with previous research that has found distinctions between the different aberrant driving behaviours (Lajunen and Summala, 2003; Sullman et al., 2002). Driving error was the clearest factor to interpret and appeared to be associated with failures of observation and judgement, while general highway violations were characterised
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by items that were a combination of traditional speeding behaviours as well as some aggressive acts. It is noteworthy that some of the highway violations that loaded on this factor may be interpreted as aggressive violations, especially for experienced professional drivers. For example, while driving especially close to a car in front of you to indicate for them to drive faster and crossing a junction knowing that the lights have already turned against you have traditionally been considered to be highway violations, they may also constitute an aggressive behaviour or at least indicate some level of frustration. Thus, behaviours traditionally viewed as highway violations may be classified as aggressive and aberrant, or at least, may originate from emotions associated with frustration. However, it is also noted that a temporality issue may be evident as aggressive violations in any current context may result in speeding violations (for example, highway violations) within a matter of moments. The third factor consisted purely of the additional items that focused on themes associated with tiredness, fatigue, loss of concentration and distraction. Taken together, the themes can be considered quite broad and thus identifying a clearly definable title for the factor proved difficult. As highlighted previously, while it was originally anticipated that fatigue and multitasking items would be represented in distinct factors, this did not occur in the current sample of drivers. Nevertheless, the writers believe that an overall component of the factor is driving while fatigued, as some form of relationship may be identified between many of the individual items and the subsequent condition of being tired and at risk of losing concentration. However, five additional questions did not load on the factors, and of particular note was that of mobile phone use. While the two items relating to ‘hands-free’ and ‘hands-held’ phone use were not clearly interpretable in the current factor structure, further research appears to be needed to determine whether this finding is specific to the current sample or if phone use is not a contemporary fleet driving issue that does not impact on driving outcomes. The corresponding calculation of Cronbach’s alpha reliability coefficients for the new factors (speeding/aggression, errors and fatigue) revealed higher reliability scores than for the traditional item loading structure calculated before the factor analytic technique.2 However, the item-loading characteristics of the current study may be influenced by a number of additional issues, such as the specific characteristics of the sample. In addition, the lack of research into fleet drivers combined with the difficulties interpreting the factor structure within the current study may indicate that individuals who drive for work, especially fleet drivers, are a special population who experience and exhibit different driving behaviours to the general motoring population. Given that the factor structure of the DBQ has varied considerably (for example, three to six factors) in different countries and different settings, situational and cultural factors need to be taken into account when utilising the DBQ (Lajunen and Summala, 2003). Finally, with regards to the prediction of self-reported driving offences and crashes, only a small proportion of the sample reported being in a crash within the last year, which contributed to difficulties identifying factors associated with the 2 Although this is to be expected, as Cronbach’s alpha usually increases with the inclusion of additional items.
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event. Subsequently, an examination of self-reported driving violations through step-wise logistic regression analysis revealed that both exposure to the road and reporting symptoms of fatigue (for example, tiredness and loss of concentration) were predictive of incurring driving violations. Firstly, exposure to the road was expected to be a significant predictor given that increasing driving distances is likely to increase the probability of deliberately or unintentionally making driving errors which may lead to demerit point loss. Secondly, fatigue was also identified as a predictor of demerit point loss and is of particular importance. Not only was driving while fatigued and driving while tired the most commonly reported behaviours by the sample, but this factor also predicted demerit point loss over and above exposure to the road. Given that feeling fatigued and making driving errors may be considered one of the most likely methods to incur infringement notices, it may not be surprising that driving under pressure and feeling tired are predictive of fines. Interestingly, there was only a moderate bivariate relationship between fatigue and exposure to the road, although it is possible this anomaly is specific to the current sample. Future research that identifies the particular reason for motorists’ demerit point loss (for example, errors versus deliberate acts) may provide for a more refined analysis to determine the specific contribution of fatigue to driving infringements and even crash involvement. Despite this, the current study provides preliminary evidence that driving under pressure and the associated feelings and behaviours of fatigue may warrant further investigation within fleet settings. A number of limitations should be taken into account when interpreting the results of this study. The response rate of participants was relatively low, but consistent with previous research utilising the DBQ scale in Australia (Dobson et al., 1999). Similar to research in this area, concerns remain regarding the reliability of the selfreported behaviour, such as the propensity of professional drivers to provide socially desirable responses. Questions also remain about the representativeness of the sample, as participants were mainly corporate fleet drivers (for example, involved in insurance sales) and such driving styles may not be easily transferable to other fleet driving populations. In summary, further research is required to establish the predictive utility of including additional items in the DBQ which are fleet specific, such as fatigue and multitasking. The present study has provided some additional preliminary evidence that modifying the DBQ to suit applied settings can produce favourable results with regards to identifying the factors that influence the driving task. References Aberg, L. and Rimmo, P. (1998). ‘Dimensions of aberrant driver behaviour.’ Ergonomics, 41, 39–56. Bianchi, A. and Summala, H. (2004). ‘The “genetics” of driving behaviour: parents’ driving style predicts their children’s driving style.’ Accident Analysis and Prevention, 36, 655–69. Blockey, P.N. and Hartley, L.R. (1995). ‘Aberrant driving behaviour: errors and
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violations.’ Ergonomics, 38, 1759–71. Davey, J., Freeman, D. and Wishart, D. (2006). ‘A study predicting crashes among a sample of fleet drivers.’ Proceedings of the Road Safety Research, Policing and Education Conference, Gold Coast, Australia [CD-ROM]. Davey, J., Wishart, D., Freeman, J. and Watson, B. (2007). ‘An application of the Driver Behaviour Questionnaire in an Australian organisational fleet setting.’ Transportation Research Part F: Traffic Psychology and Behaviour, 10: 11–21. Dimmer, A.R. and Parker, D. (1999). ‘The accidents, attitudes and behaviour of company car drivers.’ In G.B. Grayson (ed.), Behavioural Research in Road Safety IX, Crowthorne: Transport Research Laboratory. Dobson, A., Brown, W., Ball, J., Powers, J. and McFadden, M. (1999). ‘Women drivers’ behaviour, socio-demographic characteristics and accidents.’ Accident Analysis and Prevention, 31, 525–35. Lajunen, T., Parker, D. and Summala, H. (2003). ‘The Manchester Driver Behaviour Questionnaire: a cross-cultural study.’ Accident Analysis and Prevention, 942, 1–8. Lajunen, T. and Summala, H. (2003). ‘Can we trust self-reports of driving? Effects of impression management on driver behaviour questionnaire responses.’ Transportation Research Part F, 6, 97–107. Lawton, R., Parker, D., Stradling, S. and Manstead, A. (1997). ‘The role of affect in predicting social behaviours: the case of road traffic violations.’ Journal of Applied Social Psychology, 27, 1258–76. Mesken, T., Lajunen , T. and Summala, H. (2002). ‘Interpersonal violations and their relation to accident involvement in Finland.’ Ergonomics, 45 (7), 469–483. Newnam, S., Watson, B. and Murray, W. (2002). ‘A comparison of the factors influencing the safety of work-related drivers in work and personal vehicles.’ Proceedings of the Road Safety Research, Policing and Education Conference, Adelaide [CD-ROM]. Newnam, S., Watson, B. and Murray, W. (2004). ‘Factors predicting intentions to speed in a work and personal vehicle.’ Transportation Research Part F, 7, 287– 300. Ozkan, T. and Lajunen, T. (2005). ‘A new addition to DBQ: positive driver behaviours scale.’ Transportation Research Part F, 8, 355–68. Parker, D., McDonald, L., Rabbitt, P. and Sutcliffe, P. (2000). ‘Elderly drivers and their accidents: the aging driver questionnaire.’ Accident Analysis and Prevention, 32, 751–9. Parker, D., Reason, J.T., Manstead, A. and Stradling, S.G. (1995a). ‘Driving errors, driving violations and accident involvement.’ Ergonomics, 38, 1036–48. Parker, D., West, R.J., Stradling, S.G. and Manstead, A.R. (1995b). ‘Behavioural characteristics and involvement in different types of traffic accident.’ Accident Analysis and Prevention, 27(4), 571–81. Reason, J., Manstead, A., Stradling, S., Baxter, J. and Campbell, K. (1990). ‘Errors and violations: a real distinction?’ Ergonomics, 33, 1315–32. Shinar, D. ‘Aggressive driving: the contribution of the drivers and the situation.’ Transportation Research Part F, 1, 137–60. Stradling, S.G., Meadows, M.L. and Beatty, S. (2000). ‘Driving as part of your work
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may damage your health.’ In G.B. Crayson (ed.), Behavioural Research in Road Safety IX, Crowthorne: Transport Research Laboratory. Sullman, M.J., Meadows, M. and Pajo, K.B. (2002). ‘Aberrant driving behaviours amongst New Zealand truck drivers.’ Transportation Research Part F, 5, 217– 232. Wheatley, K. (1997). ‘An overview of issues in work-related driving.’ In Staysafe 36: Drivers as Workers, Vehicles as Workplaces: Issues in Fleet Management. (Report No. 9/51). Ninth Report of the Joint Standing Committee on Road Safety of the 51st Parliament. Sydney: Parliament of New South Wales. Wills, A., Watson, B. and Biggs, B. (2004). ‘The relative influence of fleet safety climate on work-related driver safety.’ Proceedings of the Road Safety Research, Policing and Education Conference, Perth [CD-ROM]. Xie, C. and Parker, D. (2002). ‘A social psychological approach to driving violations in two Chinese cities.’ Transportation Research Part F, 5, 293–308.
Chapter 18
A Comparison of Seat Belt Use Between Work Time and Free Time Driving Among Turkish Taxi Drivers Özlem Şimşekoğlu1 and T. Lajunen2 1 University of Helsinki, Finland 2 Middle East Technical University, Turkey Introduction International research has demonstrated that using a seat belt reduces considerably the number of car occupants injured or killed in traffic accidents (Elvik and Vaa, 2004; Evans, 1986). According to the results of a meta-analysis on effects of seat belt use, using a seat belt decreases the probability of being killed by about 40–50 per cent for drivers and front seat passengers, and by about 25 per cent for passengers in the back seats (Elvik and Vaa, 2004). Similarly, Evans (1986) reported that nearly 41 per cent reduction in fatalities would occur if all the front seat occupants in the US were to use lap/shoulder belts. Despite the proven effectiveness of seat belts, however, a large number of car occupants do not use seat belts in Turkey, which has been seen as one of the main reasons for low traffic safety in Turkey (SWE ROAD, 2001). Low seat belt use rate, especially among professional drivers, is a serious traffic safety problem in Turkey. For example, almost no taxi drivers, who are not required by the traffic code to use a seat belt, or front seat passengers of taxis, use a seat belt (T.C. Emniyet Genel Müdürlüğü, Trafik Hizmetleri Başkanlığı, 1999). Therefore, in order to improve driver and passenger safety, seat belt use should increase among taxi drivers and passengers. Lack of strict seat belt legislation, enforcement and frequently getting in and out of their vehicles are common reasons why taxi drivers do not use seat belts (Ferguson, Wells, Williams and Feldman, 1999; Fernandez, Park and Olshaker, 2005). In the United States, for example, considerably higher seat belt use rate (74 per cent) among taxi drivers in the District of Columbia compared with Maryland (20 per cent) and Virginia (38 per cent) was explained by the primary seat belt law, which applies to all drivers in the District of Columbia (Ferguson et al., 1999). Similarly, stricter seat belt legislation, which also applies to taxi drivers, was believed to increase the low seat belt use rate (6.8 per cent) among taxi drivers in Boston (Fernandez, Park and Olshaker, 2005). Also, taxi passengers use seat belts infrequently due to belt inaccessibility and short trips (Welkon and Reisinger, 1977; Ferguson et al., 1999). The present study investigated mainly taxi drivers’ self-reported seat belt use frequencies and reasons for not using a seat belt when driving a taxi (for example,
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during work hours) and a private car (for example, during free time). Although there are studies that investigated seat belt use among taxi drivers, we did not come across a study investigating whether seat belt use among taxi drivers changed between work time and free time driving. The main aims of the study were to investigate: •
• •
whether reported seat belt use frequencies and degree of agreement with the reasons for not using a seat belt differs according to the driver status (taxi driver, private car driver); factors related to reported seat belt use frequency when driving a taxi and a private car; and specific reasons for not using a seat belt when driving a taxi.
Method Participants One hundred and twenty-two male Turkish taxi drivers aged between 24 and 61 years (M = 39.8, SD = 9.1) participated in the study. Most of them were high school graduates (40.2 per cent), followed by elementary school graduates (27 per cent), primary school graduates (23.8 per cent) and university graduates (9 per cent). The mean number of years spent working as a taxi driver was 11.7 years (SD = 9.2). All the taxi drivers were working for different private taxi companies in Ankara. Questionnaire Taxi drivers completed the questionnaire at taxi stations while they were off work or waiting for passengers. The questionnaire included the following topics: demographic and driving related information (for example, age, education and total kilometres driven last year), seat belt use frequency in different travelling conditions, reasons for not using a seat belt, attitudes towards seat belt use, and the Driving Skills Inventory (DSI; Lajunen and Summala, 1995). Eleven seat belt use frequency items were asked for measuring the frequency of seat belt use in different travelling conditions (for example, city trips, outside city trips) on a five point Likert scale (1 = ‘never’, 5 = ‘always’). For measuring the reasons for not using a seat belt, participants were given 27 possible reasons and asked to indicate their degree of agreement with the items on a five point Likert scale (1 = ‘completely disagree’, 5 = ‘completely agree’). These items were chosen by the authors from a pool of reasons for not using a seat belt suggested by colleagues and students on a traffic psychology course. Among the 27 items, six items were only applicable to either taxi driving or private car driving, whereas the remaining 21 items were applicable to both taxi and private car driving. The questionnaire included 12 attitude items to which the respondents answered by using a semantic differential scale (1 = ‘not safe at all’, 7 = ‘very safe’). Lastly, the DSI included a five point response scale (1 = ‘absolutely weak’, 5 = ‘absolutely strong’) to measure self-reported driving skill. The participants were asked to rate items related to seat belt use frequency and reasons for not using a seat belt thinking
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of the times both when they were driving a taxi (at work) and driving a private car (in free time). Statistical analysis Factor analyses were conducted for the items asking reasons for not using a seat belt when driving a taxi and a private car, and for the DSI. Two hierarchical multiple regression analyses were also performed on the data to investigate the factors related to the reported general seat belt use frequency of the participants when driving a taxi and a private car. After entering the demographic and some driving-related variables in the first step, other variables were entered to the model using the stepwise selection method. Before the analyses, data were checked for multicollinearity and singularity. No sign of multicollinearity or singularity were found. Lastly, paired sample t-tests were conducted to compare the responses participants gave to some items both as a taxi driver and as a private car driver. Results Reported seat belt use frequencies and reasons for not using a seat belt The majority (78.7 per cent) of the participants reported that they never use a seat belt when driving a taxi, while about 21 per cent reported that they use a seat belt seldom or sometimes. However, 14 per cent of the participants reported that they never use a seat belt and 30 per cent of them reported that they always use a seat belt when driving a private car. Mean response values for the given reasons for not using a seat belt when driving a taxi and private car are given in Table 18.1. Discomfort followed by not having the habit and driving short distances was among the mostly agreed items when driving both a taxi and private car. No obligation by law to use a seat belt in city traffic was the second mostly agreed to item for the times when driving a taxi. Also, answers given to an open-ended question asking for other reasons for not using a seat belt as a taxi driver revealed that about 41 per cent of the respondents reported fear of not being able to defend themselves in case of an attack and robbery by the passengers. Factor analyses Principal axis factoring analysis with varimax rotation was applied to items related to reasons for not using a seat belt when driving a taxi and a private car and to the items of the DSI. Three factors emerged from the items related to reasons for not using a seat belt both when driving a taxi and private car. For the times when driving a taxi, the first factor was labelled as ‘situational factors’, the second factor was called ‘over trust of driving skills and experience and under estimation of accident probability’ and the third factor was labelled as ‘following the example of the other taxi drivers’. For the times when driving a private car, the first factor was labelled as ‘situational factors’, the second factor was labelled as ‘over trust of driving skills and experience
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Table 18.1 Mean response values for the reasons for not using a seat belt
Reasons
1 2 3 4 5 6 7 8 9 10 11 12 13 14
Mean response value for taxi driving*
Mean response value for private car driving* 1.42 2.11 2.61 1.39 2.08 1.55 2.58 1.88 1.22 1.75 1.71 2.11 2.31 1.85
Not believing in the effectiveness of seat belt 1.48 Fear of getting stuck in the car in an accident 2.12 Discomfort 3.50 Waste of time 1.80 No need 2.67 Low accident probability 1.62 No habit 2.86 Forgetting 1.75 Risk taking 1.20 Trust in driving experience 1.78 Trust in driving skills 1.77 Underestimation of a serious accident on city roads 2.27 Underestimation of a serious accident on short trips 2.43 Underestimation of a serious accident in good 1.93 weather 15 Underestimation of a serious accident on good roads 1.87 16 Underestimation of a serious accident on day trips 1.87 17 Driving with low speed 2.28 18 Driving a short distance 2.62 19 Airbag in the car 1.40 20 Belief in destiny 1.40 21 No obligation to use seat belt in city traffic 3.44 22 Following the example of other taxi drivers who do 1.76 not use seat belt 23 Social disapproval for seat bet use from other taxi 1.52 drivers 24 Fear of being perceived as a bad driver by passengers 1.34 25 Fear of being perceived as a bad driver by family members 26 Following the example of family members who do not use a seat belt 27 Following the example of close friends who do not use 1.38 a seat belt *1 = Completely disagree, 2 = Disagree, 3 = Neither agree nor disagree, 4 = Agree, 5 = Completely agree
1.83 1.78 2.19 2.48 1.42 1.39
1.16 1.20 1.29
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Table 18.2
Results of the factor analysis for the items related to reasons for not using a seat belt when driving a taxi and a private car When driving a taxi
3 5 6 7 9 10 11 12 13 14 15 16 17 18 19 22
23
26
27
Item Discomfort No need Low accident probability No habit Risk taking Trust in driving experience Trust in driving skills Underestimation of a serious accident on city roads Underestimation of a serious accident on short trips Underestimation of a serious accident in good weather Underestimation of a serious accident on good roads Underestimation of a serious accident on day trips Driving with a low speed Driving a short distance Airbag in the car Following the example of other taxi drivers who do not use seat belt Social disapproval for seat belt use from other taxi drivers Following the example of family members who do not use seat belt Following the example of close friends who do not use seat belt Initial Eigenvalue Explained variance (%) α/r
F1
F2
F3
When driving a private car F1 F2 F3 0.69 0.65
0.49 0.68 0.51 0.91 0.89
0.62 0.56
0.57
0.62
0.65
0.69
0.72
0.78
0.69
0.73
0.78
0.84
0.68 0.58 0.45
0.60 0.50 0.47 0.59
0.67
0.70
0.60
7.42 16.71
1.81 13.81
0.91
0.83
1.69 6.95
0.42** **p < 0.01
219
7.63 18.20
1.99 12.98
1.63 11.04
0.91
0.75
0.75
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and following the example of close others’ and the last factor was labelled as ‘not needing a seat belt, discomfort and no habit’. Items with their item loadings for the factors and internal consistency coefficient (α) or correlation coefficient (r) (in the case of only two items) of the sub-scales can be seen in Table 18.2. Similar to earlier research findings, two factors (driving skills and safety skills) emerged from the DSI. The DSI items with their item loadings on the two factors and internal consistency coefficient (α) of the sub-scales can be seen in Table 18.3. Sum variables were calculated on the basis of factor analysis results by averaging the item scores. Table 18.3 Results of factor analysis for the items of DSI Item 1 2 3 4 5 6 7 8 9 10 11 12 13 14 16 17 18 19 20
F1
F2
Fluent driving Perceiving hazards in traffic Driving behind a slow car without getting impatient Managing the car through a skid Predicting traffic situation ahead Knowing how to act in particular traffic situations Fluent lane-changing in heavy traffic Making firm decisions Staying calm in irritating situations Controlling the vehicle Keeping a sufficient following distance Adjusting your speed to the conditions Make a hill start on a steep incline Overtaking Conforming to the speed limits Avoiding unnecessary risks Tolerating other driver errors calmly Obeying the traffic lights carefully Reverse parking into a narrow gap
0.54 0.48
0.58
Initial Eigenvalue Explained variance (%)
4.98 20.25
2.69 11.64
α
0.84
0.73
0.45 0.56 0.48 0.52 0.59 0.61 0.50 0.59 0.46 0.43 0.62 0.77 0.69 0.47 0.37 0.66
Multiple regressions analyses Results of the multiple regression analysis for predicting taxi drivers’ seat belt use frequency by the study variables when driving a taxi are shown in Table 18.4. The number of accidents the drivers had experienced in last three years, driving skills and situational reasons (for example, short trips) were negatively related to the reported seat belt use frequency, whereas penalty score, safety skills and following
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the example of other taxi drivers were positively related to the reported seat belt use frequency of the participants. Table 18.4
Factors related to reported seat belt use frequency when driving a taxi
Step 1
Variable Age
R2 0.04
Change in R2 0.04
F 0.53
df 8
β –0.10
Primary school graduated
–0.15
Elementary school graduated
–0.11
University graduated
–0.12
Total km driven last year
0.10
Years working as a taxi driver
0.06
Number of accidents in last three years
–0.21*
Penalty score
0.21*
2
Driving skills
0.09
0.05
1.12
9
–0.34**
3
Safety skills
0.14
0.05
1.73
10
0.30**
4
Situational reasons
0.17
0.03
2.02*
11
–0.24*
5
Following the example of other taxi drivers
0.21
0.04
2.33*
12
0.21*
Results of the multiple regression analysis for predicting taxi drivers’ seat belt use frequency by the study variables when driving a private car are shown in Table 18.5. The factor of not needing to use a seat belt, discomfort and not having the habit of seat belt use, and total attitude towards seat belt use were negatively related to self-reported seat belt use frequency; whereas safety skills were positively related to the reported seat belt use frequency of the participants.
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Table 18.5
Step
1
Factors related to reported seat belt use frequency when driving a private car Variable
Age
R2
0.16
Change in R2
F
0.16
2.97**
df
7
Primary school graduated
β
0.12 0.07
Elementary school graduated
–0.002
University graduated
0.02
Total km driven last year
–0.15
Number of accidents in last three years
0.06
Penalty score
2
Not needing to use seat belt, discomfort and no habit
0.40
0.24
8.93***
8
0.09 –0.56***
3
Safety skills
0.43
0.03
9.21***
9
0.23**
4
Total attitude
0.46
0.03
9.21**
10
–0.19**
Paired sample tests Results of the paired sample t-tests for comparing the reported seat belt use frequencies between work driving and free time driving showed that participants reported significantly higher seat belt use frequency when driving a private car than a taxi in all travelling conditions. T-test values for the different travelling conditions are as follows: all trips in general (t(121) = –14.74, p < 0.001), city trips (t(121) = –10.58, p < 0.001), outside city trips (t(121) = –4.58, p < 0.001), short trips (t(121) = –8.30, p < 0.001), long trips (t(121) = –7.99, p < 0.001), night trips (t(121) = –10.37, p < 0.001), day trips (t(121) = –9.96, p < 0.001), trips in bad weather conditions (t(121) = –10.41, p < 0.001), trips in good weather conditions (t(121) =–9.61, p < 0.001), trips in good roads (t(121) = –9.77, p < 0.001) and trips in bad roads (t(121) = –9.90, p < 0.001). Also, results of the paired sample t-tests for comparing reasons for not using a seat belt between work driving and free time driving showed that
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participants agreed significantly more with discomfort (t(121) = 6.69, p < 0.001), waste of time due to seat belt use (t(121) =3.81, p < 0.001), no need to use a seat belt (t(121) = 4.84, p < 0.001), not having the habit (t(121) = 3.22, p < 0.001) and underestimation of accident probability in urban roads (t(121) = 2.30, p < 0.05) and short distances (t(121) = 2.13, p < 0.05) when driving a taxi than driving a private car. Discussion The present study mainly investigated whether reported seat belt use frequencies and degree of agreement with the given reasons for not using a seat belt differ between the times when driving a taxi and private car in a sample of Turkish taxi drivers. Participants reported significantly higher seat belt use frequency in all travelling conditions for the times when they were driving a private car than a taxi. This finding clearly shows that taxi drivers change their seat belt use behaviour according to their driver status (taxi driver versus car driver). Also, this finding implies that there should be reasons specific to taxi driving that explain why the same drivers use a seat belt less while driving a taxi than a private car. After discomfort, no obligation to use a seat belt in city traffic was the second mostly agreed reason for not using a seat belt when driving a taxi. Similar to the previous findings (Ferguson et al., 1999; Fernandez, Park and Olshaker, 2005), this finding emphasises the urgent need for seat belt laws to also apply to taxi drivers to increase seat belt use among them. In addition, fear of not being able to defend themselves in case of an attack and robbery by the passengers was an important motivation for not using a seat belt when driving a taxi. This finding supports a previous study emphasising the importance of workplace hazards in predicting health outcomes and safety behaviour among taxi drivers (Machin and Souza, 2004). Thus, no obligation to use a seat belt in city traffic and fear of being attacked or robbed by the passengers appear as two specific reasons for not using a seat belt when driving a taxi. When driving a taxi, driving skills were negatively related to reported seat belt use frequency, while safety skills were positively related to reported seat belt frequency. These results are in line with previous findings indicating that driving skills were positively associated with number of penalties, accidents and level of speed, while safety skills were negatively associated with these variables (Lajunen, Corry, Summala and Hartley, 1998; Özkan, Lajunen, Chliaoutakis, Parker and Summala, 2006; Sümer, 2002). Also, driving experience was positively associated with driver’s confidence in his/her driving skills (Lajunen and Summala, 1995). Thus, taxi drivers’ overconfidence in their driving skills related to their high annual mileage and exposure may partly explain their infrequent seat belt use. The number of accidents in the last three years was negatively related to reported seat belt use frequency when driving a taxi. A low number of accidents, which can be accepted as a sign of safe driving, can explain the negative relationship between number of accidents and reported seat belt use frequency. For the times when driving a private car, the factor of not needing to use a seat belt, discomfort and not in the habit was the strongest predictor of seat belt use,
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similar to previous findings (Şimşekoğlu and Lajunen, 2006). Interestingly, attitude towards seat belt use was negatively related to seat belt use indicating that as the participants had a more positive attitude towards seat belt use, their reported seat belt use frequency decreased. This finding is in line with the previous findings indicating that having positive attitudes, beliefs and intentions about using a seat belt were not strong predictors of actual seat belt use all the time (Chliaoutakis et al., 2000; Knapper, Cropley and More, 1976; Loo, 1984). The present findings have some practical implications for the interventions aiming at increasing seat belt use among taxi drivers. Using a seat belt is crucial for taxi drivers for two main reasons. First of all, taxi drivers are at a higher risk of being involved in a car accident due to their high driving exposure. Secondly, travelling by taxi is one of the most common public means of transportation among Turks. Therefore, taxi drivers should feel responsible for the safety of their passengers by using a seat belt and requiring their passengers to use a seat belt. The present findings clearly show that strict seat belt laws with high enforcement are needed to increase seat belt use among taxi drivers. Also, in taxis, the presence and availability of seat belts should be controlled by the vehicle inspection agencies or the police. Besides the laws, seat belt campaigns aiming at increasing seat belt use among taxi drivers should be more common. These campaigns should emphasise the safety benefits of seat belt use in all travelling conditions and include messages to taxi drivers about being a responsible and safe driver who is concerned for public safety. Acknowledgements This study has been supported by the Graduate School of Psychology in Finland and Marie Curie Transfer of Knowledge programme (‘SAFEAST’ Project No: MTKDCT-2004-509813). References Chliaoutakis, E.J., Gnardellis, C., Drakou, I., Darviri, C. and Sboukis, V. (2000), ‘Modelling the factors related to the seatbelt use by the young drivers of Athens.’ Accident Analysis and Prevention, 32, 815–25. Elvik, R. and Vaa, T. (2004). The Handbook of Road Safety Measures. Amsterdam: Elsevier. Evans, L. (1986). ‘The effectiveness of safety belt in preventing fatalities.’ Accident Analysis and Prevention, 18, 229–41. Ferguson, S.A., Wells, J.K., Williams, A.F. and Feldman, A.F. (1999). ‘Belt use rates among taxicab drivers in a jurisdiction with license points for nonuse.’ Journal of Safety Research, 30, 87–91. Fernandez, W.G., Park, J.L. and Olshaker, J. (2005). ‘An observational study of safety belt use among taxi drivers in Boston.’ Annals of Emergency Medicine, 45, 626–9. Knapper, K.C., Cropley, J.A. and Moore, J.R. (1976). ‘Attitudinal factors in the nonuse of seat belts.’ Accident Analysis and Prevention, 8, 241–6. Lajunen, T., Corry, A., Summala, H. and Hartley, L. (1998). ‘Cross-cultural
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differences in drivers’ self-assessment in their perceptual motor and safety skills: Australians and Finns.’ Personality and Individual Differences, 24, 539–50. Lajunen, T. and Summala, H. (1995). ‘Driver experience, personality, and skill and safety-motive dimensions in drivers’ self assessments.’ Personality and Individual Differences, 19, 307–18. Loo, R. (1984). ‘Correlates of reported attitudes towards and use of seat belts.’ Accident Analysis and Prevention, 16, 417–21. Machin, M.A. and De Souza, J.M.D. (2004). ‘Predicting health outcomes and safety behaviour in taxi drivers.’ Transportation Research Part F, 7, 257–70. Özkan, T., Lajunen, T., Chliaoutakis, J.E., Parker, D. and Summala, H. (2006). ‘Cross-cultural differences in driving skills: a comparison of six countries.’ Accident Analysis and Prevention, 38, 1011–18. Sümer, N. and Özkan, T. (2002). ‘The role of driver behavior, skills and personality traits in traffic accidents.’ Turkish Journal of Pyschology, 17(50), 1–25. SWE ROAD (2001). Türkiye için ulusal trafik güvenliği programı. Taslak Nihai Rapor. Ankara. Şimşekoğlu, Ö. and Lajunen, T. (2006). ‘Why Turks do not use seat belts?’ An interview study. Submitted. T.C. Emniyet Genel Müdürlüğü, Trafik Hizmetleri Başkanlığı. (1999). Ülkemizde emniyet kemeri kullanımı. Ankara: Trafik Araştırma Merkezi Müdürlüğü Yayınları. Welkon, C. and Reisinger, K.S. (1977). ‘The phantom taxi seat belt.’ Public Health Briefs, 67, 1091–2.
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Chapter 19
A Review of Developing and Implementing Australian Fleet Safety Interventions: A Case Study Approach Update Darren Wishart, Jeremy Davey and James Freeman Centre for Accident Research and Road Safety, Queensland (CARRS-Q), Australia Introduction There is relatively little research in Australia or overseas devoted to the area of fleet and work-related road safety, and this is being increasingly addressed. The recent focus is in part due to an awareness of workplace health and safety issues and the overall impact that fleet-related safety issues have on business effectiveness and road safety (Downs et al., 1999; Haworth et al., 2000). Historically, costs associated with work-related vehicle crashes have more often than not been calculated in terms of vehicle damage or write off costs. Murray et al. (2003) suggest that the direct cost of crashes in terms of repairs is only the tip of the iceberg. In recent years, changes in industry/employer accountability, business processes, occupational health and safety, workers’ compensation legislation, insurance and third party coverage, and a generally more litigious environment, require industry to develop better benchmarking along with more comprehensive programmes to improve fleet safety. There is currently only a small amount of work in this area and estimates of the true cost for work-related crashes suggest that hidden costs may be somewhere between 8–36 times vehicle repair or replacement costs (Murray et al., 2003). Based solely on workers compensation data, estimates of costs to Australian industry for workrelated crashes have been in the vicinity of $400–$500 million per year (Wheatley, 1997). Furthermore, a recent estimate of the average cost to society for a fatal crash is approximately $2 million (Austroads, 2006) and the average total insurance cost of a fleet incident to organisations and society is approximately $28 000 (Davey and Banks, 2005). Previous research has highlighted work-related road safety as an area that requires further attention with a focus on developing research informed interventions aimed at improving road safety outcomes and, in turn, offering huge financial savings to industry and the community (Bibbings, 1997; Murray et al., 2003; Haworth et al., 2000; Staysafe, 1997). In Australia, road crashes are the most common cause of workrelated injury, death and absence from work (Howarth et al., 2000). Work-related traffic injuries are about twice as likely to result in death or permanent disability
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than other workplace accidents (Wheatley, 1997) and account for up to 26 per cent of work-related fatalities in Australia and 13 per cent of the national road toll (Murray et al., 2003). There is an obvious and growing need for industry, government and the community to allocate resources and build knowledge and expertise in this area. Historically, in terms of exploring and implementing fleet safety interventions, industry has often taken a ‘silver bullet’ approach aimed at developing and implementing a single countermeasure or intervention strategy to encompass and address all fleet-related road safety issues. This approach is often reactive, rather than proactive which aims to not only reduce similar incidents but also is aimed at improving behaviour. One shortcoming with a reactive approach is that often the single implemented countermeasure results in only a short-term fix and does not address the underlying contributing behavioural factors relating to the crash. Thus the organisation embarks on a cyclical process similar to a dog chasing its tail and may not demonstrate significant improvement in their fleet safety records over time. More recently one of the facilitators of progress in fleet safety has been the occupational health and safety domain (OHS). OHS and chain of responsibility (COR) legislation has helped to create further awareness of an organisation’s responsibility to ensure safe work practice. Industry, as a means of trying to address OHS responsibilities in fleet safety, adopts what they consider to be a best practice approach. Historically, best practice to improving fleet safety has often meant any practice or type of intervention being implemented. This can result in countermeasures and intervention strategies that have not been previously evaluated or organisations not implementing a thorough and empirical evaluation process. Furthermore, the silver bullet approach is no longer used in other areas of road safety, as research would suggest that intervention approaches need to be proactive and multi-dimensional. For instance, strategies and interventions to reduce the incidence of drink driving often involve not only law enforcement and random breath testing, but also incorporate advertising and awareness campaigns, rehabilitation programmes and technological interventions such as alcohol interlock devices. However, the current state of fleet safety has many organisations not addressing the work-related road safety issue as comprehensively as other work-related safety risk issues within their workplace. For example, organisations often allocate more safety-related resources to lower exposure and lower workplace risk processes, in contrast to the high exposure and high risk of work-related driving. In attempting to satisfy legislative needs of OHS, organisations plan the development of work-related road safety intervention strategies, although the reality within the majority of organisations is that they often struggle to implement such interventions. The failure to effectively implement fleet safety interventions often stems from a lack of management commitment and support, and general underresourcing. Thus there is an immense discrepancy between what organisations plan to do and what is actually undertaken in addressing work-related road safety risks and initiatives.
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Factors of influence in fleet driver behaviour A proactive multi-dimensional approach to fleet safety is required to help address the many factors that influence fleet driver behaviour. The following figure provides an indication of the numerous conditions influencing driver behaviour and subsequently fleet driver behaviour (Lonero and Clinton, 1998). Historically, fleet safety initiatives, in part due to fleet safety coming from an asset management perspective, have taken on a one size fits all approach. This approach has often been lacking in addressing the varied influences underlying fleet driver behaviour often resulting in only shortterm fleet safety improvement.
Figure 19.1 Conditions influencing driver behaviour Source: Lonero and Clinton, 1998.
Case study research Research conducted by CARRS-Q with a variety of industry fleets reveal similar patterns emerging across fleets in relation to causal and contributing factors to crashes, data recording and reporting issues, types of crashes and the types of vehicles involved. Throughout a number of large diverse vehicle fleets the most common types of crashes accounting for the vast majority of fleet incidents are represented by: • reversing • rear enders • road conditions • loss of control
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animal-related incidents damage whilst parked accumulated damage
Interestingly, these crash categories appear to be a reflection of a combination of a blameworthy and asset management approach to crashes. Categorisation in this manner does not provide any insight into the perceptions, attitudes, safety climate and organisational culture contributing to crashes through the influence on human behaviour. In contrast, transport authorities’ recording of crashes indicate a broader range of contributing factors to crashes which encompasses driver and road conditions. For example, Queensland Transport (2001) lists factors contributing to crashes such as: • • • • • • • • • • • • •
disobeying road rules alcohol/drugs speed inexperience inattention age fatigue other driver conditions negligence rain/wet road road conditions vehicle defects street lighting
These two approaches to recording crashes demonstrate the different genres of approaches to fleet safety within organisations; one being asset management and the second having more of a human behaviour interface. Each method of recording crashes provides different types of information that can be used to inform organisational objectives and interventions. The asset management approach is the most widely used approach to inform interventions. Whilst this may often result in short-term financial gain, it does not supply the information necessary for large-scale behavioural interventions and workplace culture change. The alternate approach used in other domains focuses more on driver behaviour and road conditions. These two approaches to data collection which inform interventions are reactive in that the core data collection occurs post crash. What is needed is a data collection approach that centres on driver behaviour and subsequently influences safer workrelated driving. The majority of current approaches in the workplace, while helpful to an organisation in some sense, do not provide the information necessary to implement targeted interventions designed to address the specific behavioural, attitudinal and cultural influences impacting on work-related road safety. In addition, the current reactive data collection approaches also do not provide an effective manner in which to empirically evaluate fleet safety interventions and initiatives that are implemented.
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For example, the collected data are often more reflective of insurance recording mechanisms which incorporate the process of drivers ‘attributing blame’ elsewhere rather than the objective identification of the factors contributing to the crash. Historical approach One of the historical approaches to fleet safety focuses on ‘behind the wheel’ driver training and education. Although many of these programmes are to teach road users the skills necessary for the successful operation of a vehicle on our roads, caution needs to be exercised to ensure that the distinction between performance and behaviour is recognised, as is the difference between what road users are capable of doing and what they actually do. Performance levels of road users can often be linked to the skills and demands of certain road situations, whereas road user behaviour is often influenced by cultural, personality, attitudinal and motivational factors (Parker, Lajunen and Stradling, 1998). This suggests that high levels of skill or proficiency in a task does not necessarily translate into better behaviour. There is also a common misunderstanding that improving road user skills will automatically improve road user behaviour, which in turn is expected to result in improved road safety. Furthermore, increased skill proficiency needs to be complimented by organisational processes and procedures that support safe driving behaviour. For example, although training may provide the skills and possible awareness to drive safely (for example, to not speed), organisational processes and work tasks may create time pressure demands that compromise safe driving operations. Driver training and education programmes involving a strong practical component, such as that the development of vehicle control skills may inadvertently create an inflated belief in one’s own driving ability, which in turn may lead to an increase in aggressive driving behaviour (Katila, Keskinen, Hatakka and Laapotti, 2003). In order to improve fleet safety, organisations need to adopt a broader perspective and develop initiatives targeted at the underlying cultural issues further influencing fleet safety, along with adopting the necessary supportive organisational processes that facilitate safe driving. Cultural approach Recent research conducted across various vehicle fleet settings suggests there is a strong influence on work-related driving behaviours by an organisation’s safety climate (Wills, 2003). Safety climate can be expressed as an employee’s psychological perceptions of safety culture and practice (Hayes et al., 2002). These perceptions are developed from the employee’s continual observation of other work colleagues’ safety practice. These observations in turn influence employee behaviour in relation as to what are considered accepted levels of safety required to perform work-related tasks (Varonen and Mattila, 2000). An example of the influence that organisational culture and safety climate can have on performance can be demonstrated through the practice of speeding. There is a strong focus on road safety and educational campaigns highlighting the dangers
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of speeding and the need for drivers to obey speed limits, yet enforcement data demonstrate that speeding still frequently occurs. Organisational culture within a fleet setting may dictate that it is more important to attend an appointment on time or complete a ‘necessary’ task urgently, than it is to be late or leave a task incomplete. In this instance the employee may compromise their safety and the safety of others by driving above the speed limit in order to ‘make up time’ or ‘deliver the goods’. Needs analysis Organisations embarking on a programme of improving fleet safety often undertake a needs analysis investigating what is currently being done in relation to addressing fleet safety issues. This process often involves investigation into areas such as: • • • • • • • •
organisational process interventions reporting recording policy recruitment interventions evaluation
The results of a needs analysis are often used by organisations to assist in identifying areas for improvement and to ensure that appropriate processes, mechanisms and structure are adequately in place to support change and intervention strategies. However, the information provided by the needs analysis often exposes deficiencies in processes, reporting, recording and policy mechanisms without actually informing the design of behavioural-based intervention strategies. Future fleet safety research and the subsequent development of intervention programmes must address the influences on behaviour to achieve long-term improvements in fleet safety. Fleet safety research has previously been lacking in developing research-based and informed intervention strategies directed at behaviours, attitudes, intentions, perceptions, organisational culture and safety climate. It is with this in mind that current research should be directed at addressing a number of domains that influence behaviour. The results obtained from baseline measures in these domains should guide the development and implementation of targeted interventions aimed at high risk sectors and behaviours in an operational fleet environment. Identified baseline measures Organisations need to gather baseline measures from a number of areas that current research has identified as influencing the design, development and implementation of appropriate and targeted intervention strategies. These can include:
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driver attitudes road safety knowledge behavioural intentions perceptions risk taking sensation seeking crash records driver history safety climate.
Current research undertaken by CARRS-Q is examining the development of targeted intervention strategies tailored toward specific issues identified from baseline measures in the above-mentioned areas. The results obtained from these baseline measures are used to assist organisations in making informed choices regarding the implementation of countermeasures. High risk areas of vehicle fleets can be identified from baseline measures not only in terms of vehicle types and geographical location, but also in relation to influences of human behaviour, perceptions, attitudes, personality traits, beliefs, safety climate and organisational culture. Once identified, these high risk sectors assist the design and implementation of appropriate intervention strategies. As the implementation of intervention strategies and their subsequent results often takes time, a further advantage of appropriate baseline measures is that any countermeasures and interventions implemented can be evaluated against changes across a wide variety of performance indicators. For example, an intervention strategy may not demonstrate initial improvements in crash rates but may demonstrate improvements in cultural influences of behaviour and attitudes, which in both the short and longer term can lead to improvements in vehicle fleet safety. Conclusion In summary, this chapter has highlighted some of the major issues that are influencing fleet safety intervention development and implementation in Australia. The importance of undertaking a baseline measurement approach along with accurately measuring driving behaviour has been identified as a crucial element of the development and implementation of work-related road safety initiatives. It is necessary for future fleet safety improvements that organisations and researchers work collaboratively to ensure that fleet intervention strategies are research based and aimed at developing targeted interventions toward the numerous high risk sectors and influences on fleet driver behaviour. However, it remains of concern that organisations are reluctant to adequately resource and implement fleet safety interventions that have been tailored to reduce their specific work-related road safety risks. Despite such difficulties, continued efforts to develop, implement and evaluate effective fleet safety interventions can only contribute to the reduction in the burden of work-related road trauma.
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References Austroads (2006). Guide to Road Safety, Part 1: Road Safety Overview. Sydney, Australia. Bibbings, R. (1997). ‘Occupational road risk: towards a management approach.’ Journal of the Institution of Occupational Safety and Health, 1, 61–75. Downs, C.G., Keigan, M., Maycock, G. and Grayson, G.B. (1999). The Safety of Fleet Car Drivers: A Review. No. 390. Crowthorne: Transport Research Laboratory. Haworth, N., Tingvall, C. and Kowadlo, N. (2000). Review of Best Practice Road Safety Initiatives in the Corporate and/or Business Environment. No. 166. Clayton: Monash University Accident Research Centre. Hayes, B.C., Bartle, S.A., and Major, D.A. (2002). ‘Climate for opportunity: a conceptual model.’ Human Resource Management Review, 12, 445–468. Joint Standing Committee on Road Safety (1997). Staysafe 36: Drivers as Workers, Vehicles as Workplaces: Issues in Fleet Management. Ninth report of the Joint Standing Committee on Road Safety of the 51st Parliament. (Report No. 9/51): Parliament of New South Wales. Katila, A., Keskinen, E., Hatakka, M. and Lapotti, S. (2003). ‘Does increased confidence among novice drivers imply a decrease in safety: the effects of skid training on slippery road accidents.’ Accident Analysis and Prevention, 36(4), 543–50. Lonero L.P. and Clinton K.M. (1998). Changing Road User Behaviour: What Works, What Doesn’t. Toronto: PDE Publications. Murray, W., Newnam, S., Watson, B., Davey, J. and Schonfeld, C. (2003). Evaluating and Improving Fleet Safety in Australia. ACT: Australian Transport Safety Bureau. Parker, D., Lajunen, T. and Stradling, S. (1998). ‘Attitudinal predictors of interpersonally aggressive violations on the road.’ Transportation Research Part F, 1, 11–24. Queensland Transport (2001). Road Traffic Crashes in Queensland. Brisbane: Queensland Government. Varonen, U. and Mattila, M. (2000). ‘The safety climate and its relationship to safety practices, safety of the work environment and occupational accidents in eight wood-processing companies.’ Accident Analysis and Prevention, 32, 761–9. Wheatley, K. (1997). ‘An overview of issues in work-related driving.’ In Staysafe 36: Drivers as Workers, Vehicles as Workplaces: Issues in Fleet Management. (Report No. 9/51). Ninth report of the Joint Standing Committee on Road Safety of the 51st Parliament. Sydney: Parliament of New South Wales. Wills, A. (2003). ‘Work-related driver behaviour and intentions: the influence of fleet safety climate.’ Submitted honours thesis, Queensland University of Technology, Queensland Australia.
Chapter 20
Designing a Psychometrically Based Self-Assessment to Address Fleet Driver Risk Lisa Dorn and Julie Gandolfi Cranfield University, UK Introduction Drivers of company vehicles are more likely to be involved in crashes than private motorists, even when exposure has been accounted for (Chapman, Roberts and Underwood, 2000; Broughton et al., 2003). As many as one in three road deaths may involve someone driving while at work. This group includes not just those who drive for a living – bus, coach and goods vehicle drivers – but also company car drivers driving to or from a meeting or appointment. This latter group may not see driving as the key activity of their working day. It is, however, the time when they are most at risk while at work. There are many possible reasons why this might be the case (for a review, see Lancaster and Ward, 2002). Research has suggested that at-work drivers are more vulnerable to fatigue brought about by driving for long periods of time (Horne and Reyner, 1995; Maycock, 1996). Other studies suggest that driving under time pressure to meet schedules is one of the main contributors to at-work crashes (AdamsGuppy and Guppy, 1995; Downs, Keigan, Maycock and Grayson, 1999). Several large Health and Safety Executive surveys have indicated that stress and related conditions formed the second most commonly reported group of work-related illhealth conditions affecting 420 000 people in Great Britain in 2005–2006 (www. hse.gov.uk/statistics/). It is certainly the case that company car drivers are more likely to engage in risky driving behaviours compared with that of private motorists (Downs et al., 1999; Broughton et al., 2003; Chapman, Roberts and Underwood, 2000; Parker, West, Stradling and Manstead, 1995; Stradling, 2000). The increased risk of work-related crashes may well be due to a combination of both driver stress and fatigue caused by the pressure to meet tight deadlines and schedules whilst at work. In a sense, the car has become a place of work in which their response to stress affects the way they drive. According to the Transactional Model of Driver Stress, stress outcomes are generated by the driver’s cognitive appraisals that the demands of the task stretch or exceed the driver’s capabilities and coping resources (Gulian et al., 1989). Several distinct cognitive processes contribute to an evaluation of this kind, including appraisal of task demands and personal competence, and selection and
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implementation of coping strategies. Two instruments have been developed with the Transactional Model as their theoretical underpinning called the Driver Stress Inventory (DSI) and the Driver Coping Questionnaire (DCQ). The DSI (Matthews et al., 1996) measures five dimensions of vulnerability to driver stress: aggression items, concerning feelings of anger and frustration and willingness to engage in risky behaviours such as tailgating and frequent overtaking; dislike of driving, associated with anxiety about driving and lack of enjoyment and confidence; hazard monitoring, related to active search for danger and threats, even when driving seems routine; fatigue-proneness describes vulnerability to physical and mental fatigue symptoms; and thrill-seeking relates to enjoyment of risk and danger. The DSI attained good psychometric properties (Matthews et al., 1997) with reliable scales distinct from standard personality traits. The DSI predicts driver behaviour criteria in British, American and Japanese samples (Matthews et al., 1997; Matthews, 2002). Aggression and thrill-seeking are associated with increased rates of accident involvement, convictions for traffic offences and self-reported violations and errors. Dislike of driving relates to increased risk of driving errors accompanied by fewer speeding convictions. The three dimensions most predictive of stress states are dislike of driving, aggression and fatigue proneness; they are similarly correlated with state dimensions in both simulator and field studies (Matthews et al., 1998; Matthews, 2001; 2002). Dislike of driving is associated with both distress and worry, mediated by task-related cognitive interference and correlated with negative selfappraisal. In combination, these processes create negative moods and anxiety and have a detrimental effect on task performance (Matthews, 2001; Dorn and Matthews, 1995). Aggression and fatigue-proneness also tend to relate to poorer mood, but aggression is most strongly correlated with anger and fatigue-proneness with scales linked to low task engagement and fatigue. The DCQ (Matthews et al., 1996) was developed to assess individual differences in coping, measuring five dimensions including: confrontive coping (for example, ‘showed other drivers what I thought of them’), task-focused (for example, ‘made sure I avoided reckless actions’), emotion-focused (for example, ‘blamed myself for getting too emotional’), reappraisal (for example, ‘tried to gain something worthwhile from the drive’) and avoidance (for example, ‘cheered myself up by thinking about things unrelated to the drive’). Drivers high in confrontive coping tend to make unsafe decisions (Matthews, 2001) and it is the strongest single predictor of tailgating (Matthews, 2005), as well as violations and minor accidents (for example, Matthews et al., 1996). On the other hand, task-focused coping is a more adaptive strategy, such as deliberately controlling speed, and is more likely to be used by drivers high in hazard monitoring. So how do companies manage their at-work road risk? Mostly employees are given one-to-one skills-based driver training but behavioural responses to stress and fatigue whilst driving is not addressed with this particular intervention. Driver education is increasingly focusing on raising awareness about risk and getting drivers to evaluate their abilities (Sundstrom, 2005) and personal strengths and weaknesses (Vissers et al, 2007). Educating drivers needs to take a far broader approach and consider self evaluation methods and self-analysis (Hatakka et al, 2002) so that they can understand their personal risk better. To illustrate, Gregersen et al (1996)
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has found stronger evidence of a safety benefit amongst company car drivers using group discussion techniques compared with driver training. When it comes to educating at work drivers, we suggest that the main focus should be on how to manage driver stress and fatigue, but a driver trainer needs a way of understanding an individual’s vulnerability to driver stress and what kind of coping strategies they might employ. Coaching can then be delivered specific to their needs. Self-assessments of driver behaviour have several advantages then. Individuals can report their beliefs, feeling and behaviours in a way that the driver trainer cannot be privy to. Self-assessments direct attention towards areas for improvement. Any instrument for this purpose must be capable of determining whether the individual is claiming competence in their driving, when they may not possess it. When people provide a self-assessment they know that their responses are going to impact on them in some way. Hence, drivers may respond with what they think is an appropriate answer, rather than how they really behave. It is well known that drivers tend to rate themselves as better drivers than their peers, particularly young male drivers. However, in a review of studies, Shrauger and Osberg (1981) found that self-assessments were as good if not better than external ratings and scores. We therefore considered it important in the development of a self-assessment instrument for company car drivers to incorporate a measure of socially desirable responding, using an adapted version of the Driver Social Desirability Scale (DSDS: Lajunen and Summala, 1997). The DSDS measures two factors called impression management and self deception. The impression management scale refers to a deliberate tendency to give favourable self descriptions. The self deception scale is defined as a positively biased but subjectively honest self description. In an earlier version of the DSI, impression management was negatively related to self-reported accidents, penalties, overtaking frequency, speeding and aggression and positively related to rule compliance. Self deception was correlated positively with control in traffic. Hitherto, we have been unable to find studies that have considered driver stress and coping amongst company car drivers, nor whether they are prone to socially desirable responding. This paper reports the results of two studies. Study 1 aims to report the results of a factor analysis of at-work drivers’ responses to the three instruments as potentially useful predictors of work-related driver behaviour. This will be achieved by investigating whether the factor structure of the instruments are relevant for this particular group with the aim of incorporating these instruments into one measure if this is found to be the case. Study 2 reports a validity analysis of the refined measure by assessing age and sex differences in driver stress, coping and socially desirable responding. The relationship between violation and crash history and responses to the scales will also be presented to determine whether there is evidence that the refined instrument is predictive of an external measure of at risk behaviour. Finally, preliminary analyses of the validity of self-assessment using two internal criteria, impression management and self deception, will be reported.
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Method: Study 1 Participants Three hundred and thirty-four company car drivers completed the first part of this study. All participants were employed by companies as sales representatives. Age ranged from 18 to 74 years (Mean = 41.26, SD = 10.95). One hundred and three participants were female and 230 were male. Descriptive statistics for demographic and situational data are provided in Table 20.1. Table 20.1 Participant data
Age Mileage Hours driving per week Years licence held Points on licence Number of collisions in previous three years Number of weeks of post-licensed driver training received
Mean 41.26 17 840 7.54 18.94 0.06 0.2
SD 10.95 13 060 8.573 9.64 1.43 0.496
3.92
1.53
Procedure Three separate questionnaires were administered at one sitting taking about 20 to 40 minutes to complete online and participants were informed that their data would be confidential. Questionnaire A consisted of all 48 items of the DSI and 40 newly constructed items based on in-depth interviews and a literature review of the risks of driving for work. Questionnaire B consisted of 34 of the original 35 DCQ items. The item not included was ‘Thought about the consequences of having an accident’, based on responses from a pilot study in which at-work drivers considered this item to be not appropriate. Questionnaire C incorporated 12 items based on the DSDS. Treatment of results There are two alternative analyses that could be used to analyse the questionnaire data – principal components analysis (PCA) and factor analysis (FA). The techniques are very similar, as they both aim to produce a set of linear combinations of items that represent the underlying constructs within a data set (Pallant, 2001). However, they are different in the way they do this. PCA uses all the variance in the variables as it produces the linear combinations, while FA estimates factors using a mathematical model which only analyses shared variance (Tabachnick and Fidell, 1996). Guadagnoli and Velicer (1988) reviewed the literature on PCA and Factor Analysis (FA), and reported that the solutions created by PCA and FA were largely very similar. Critics cite the increased error found in PCA to argue in favour of
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allegedly more rigorous FA methods, but in this study the data relate to a questionnaire comprised of well-established factors measuring driver behaviour, therefore it is not necessary to estimate factors based on a mathematical model – instead, the focus is on identifying whether the existing variables group together in the same way for company car drivers as they do for commuter drivers. PCA establishes the linear components that exist within the data and how each variable contributes to a component or factor (Field, 2000). It produces a set of correlations between each item and the factor onto which it loads, which enable analysis of the extent to which an item contributes to the factor. Once a number of factors have been established, interpretation is assisted through rotation of factors. This does not alter the underlying constructs, but it presents the loadings in a more interpretable manner. Oblique rotation is used when factors are highly correlated. When the items are plotted on a graph it allows the axes to move closer together or further away, so that the factor represented by the axis can intersect the items when keeping the axes at 90 degrees would not. Orthogonal rotation keeps the axes in the same position but allows them to rotate until clusters of items have been intersected sufficiently to form factors. When factors are uncorrelated, the clusters are less likely to plot close to one another so keeping the axes perpendicular to one another is suitable, and produces a more interpretable factor solution. An orthogonal rotation (Varimax) was chosen for this analysis, although in the development of the original DSI an oblique rotation was used. Field (2000) suggests running the analysis using both kinds of rotation and choosing the most appropriate solution. Both procedures were carried out, and minimal differences between the two factor structures were found. When oblique and orthogonal rotations create a very similar structure, choosing the orthogonal rotation is supported by Kline (1994), who states: ‘Varimax is an excellent method of reaching orthogonal simple structure, and in many cases oblique solutions are virtually identical.’ Shaffer and Sinnett (1964) state that, ‘agreement between two such diverse methods of rotation would increase confidence in the simple structure solution’ and go on to explain that in their factor analysis of ratings of personality theories, such similarity in factor structure despite different rotations provides a simple structure which is easily interpreted. Field (2000) states that an oblique rotation should only be used if there are good reasons to suppose that the underlying factors are related in theoretical terms. In the development of the original DSI, there was evidence that the factors of its forerunner, the Driver Behaviour Inventory (DBI: see Gulian, Matthews, Glendon, Davies and Debney, 1989) were highly correlated for commuter drivers. However, it is reasonable to suppose that the underlying mechanisms behind company car driving are quite different from those of commuters and the same relationships between factors should not be assumed. Results PCA used for this analysis has three main advantages: firstly to understand the structure of a group of variables; secondly to divide variables such as questionnaire items into groups relating to different constructs; and thirdly to reduce a large data
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set to a reasonable size without losing the essence of the data. PCA was used in the development of the original DSI (Matthews et al., 1996; 1997) and is therefore deemed appropriate for this analysis. Analyses revealed that the value produced by the Kaiser-Meyer-Olkin test of sampling adequacy (Kaiser, 1970) for the PCA exceeded 0.6 and thereby indicated that the sample was large enough to provide reliable PCA results (KMO = 0.858). Bartlett’s test of sphericity was also significant (Sig. = < 0.0005) which indicated that the data set was suitable for PCA (Bartlett, 1954). The purpose of the PCA analyses was to test whether the original factor structure for these measures was reproduced in our data (Gandolfi and Dorn, 2005; Garwood and Dorn, 2005). Varimax rotation was used to provide a simple structure with easily interpretable factors. Questionnaire A The PCA for Questionnaire A showed that six factors explained 46.31 per cent of the variance. Of the six factors that emerged, five were largely replicated factors from the DSI – aggression, thrill-seeking and hazard monitoring were replicated. Fatigueproneness reversed to form fatigue resistance consistent with a factor analysis of the DSI for police drivers (Gandolfi and Dorn, 2005) and dislike of driving reversed to form the enjoyment of driving factor, consistent with analyses of previous DSI responses for both police drivers and bus drivers (Gandolfi and Dorn, 2005; Garwood and Dorn, 2005). An additional factor also emerged and was labelled work-related risk. This factor describes the extent to which company car drivers report taking risks when under time pressure. The work-related risk factor is similar to the risk inevitability factor identified in a sample of police drivers (Gandolfi and Dorn, 2005). An internal consistency analysis was conducted using Cronbach’s alpha coefficient to establish the internal reliability of each of the six scales shown in Table 20.2. The PCA and the internal consistency analysis removed 38 of the 88 items. Table 20.2 Factor structure of Questionnaire A post-PCA
Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6
Number of items 12 items 11 items 7 items 9 items 6 items 5 items
Cronbach’s alpha 0.896 0.874 0.863 0.763 0.857 0.641
Factor label Thrill-seeking Fatigue resistance Enjoyment of driving Work-related risk Hazard monitoring Aggression
Questionnaire B The PCA of Questionnaire B revealed that five factors explained 50.44 per cent of the variance. The five factors of the original DCQ were replicated – task focused, avoidance, reappraisal, emotion focused and confrontive coping. These factors were
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also subjected to internal consistency analysis and presented in Table 20.3. The PCA and internal consistency analysis removed nine of the 34 original items. Table 20.3 Factor structure of Questionnaire B post-PCA
Factor 1 Factor 2 Factor 3 Factor 4 Factor 5
Number of items 8 items 5 items 7 items 5 items 5 items
Cronbach’s alpha 0.860 0.847 0.756 0.699 0.727
Factor label Task focus coping Confrontive coping Avoidance coping Emotional coping Reappraisal coping
Questionnaire C The PCA of the socially desirable responding items showed that two factors explained 55.60 per cent of the variance – and labelled impression management and driver confidence. The PCA and internal consistency analysis removed two of the 12 items. The Cronbach’s alphas are shown in Table 20.4. Table 20.4
Factor 1 Factor 2
Factor structure of Questionnaire C post-PCA Number of items 6 4
Cronbach’s alpha 0.840 0.852
Factor name Impression management Driver confidence
Method: Study 2 Given the findings of the PCA reported in Study 1, there was a firm rationale to incorporate the remaining items to form a new psychometric instrument to measure company car driver behaviour for self-assessment and training purposes. We named this instrument the fleet driver risk index (FDRI). As part of the development of the FDRI, an investigation of its validity was undertaken using a fresh sample. Participants and procedure A sample of 1089 completed the new online fleet driver risk index containing 90 items. Participants ranged in age from 20 to 71 years (mean = 41.05, sd = 9.28). Eight hundred and fifty-two of the participants were male and 237 were female. All participants were employed by several UK companies as sales representatives. Further demographic and situational information is provided in Table 20.5.
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Table 20.5 Participant data
Age Mileage Hours driving per week Years licence held Points on licence Number of collisions in previous three years Number of weeks of post-licensed driver training received
Mean 41.05 23 245 13.60 18.64 0.75 0.36
SD 9.28 15 361 12.63 8.72 1.52 0.73
3.72
1.72
Table 20.6 Gender differences in FDRI factors Factor Aggression Thrill-seeking Enjoyment of driving Hazard monitoring Driver confidence
F 0.423 7.954 2.622 6.833 1.668
Sig. 0.001 0.000 0.005 0.000 0.000
Male (mean) 37.20 23.38 68.83 84.71 66.68
Male (sd) 17.02 16.71 14.91 13.17 19.39
Female (mean) 41.41 17.93 65.73 80.58 60.88
Female (sd) 17.70 14.56 15.97 14.24 19.46
Results and discussion Gender differences in FDRI factors Firstly, t-tests were conducted to establish gender differences in the FDRI factor. (See Table 20.6) Higher levels of aggression reported by female company car drivers may be due to role conflict with professional women having to cope with the demands of working in a competitive sales environment as well as family life. Male drivers’ higher mean scores for thrill-seeking, enjoyment of driving and hazard monitoring were consistent with previous findings for the DSI (Matthews et al., 1996) and males’ higher mean score for driver confidence was also consistent with expectations (Lajunen and Summala, 1997). Age, experience and driver stress and coping Given there were significant correlations between age and the factor scores, partial correlations controlling for age was conducted. The findings revealed that, consistent with previous research on commuter drivers (Matthews et al., 1997), aggression was strongly correlated with thrill-seeking (r = 0.37, p < 0.001) and confrontive coping (r = 0.46, p < 0. 001).
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When controlling for age, experience was significantly correlated with thrillseeking (r = 0.10, p < 0.001) and reappraisal coping (r = 0.13, p < 0.001), suggesting that more experienced company car drivers report higher levels of thrill-seeking. Perhaps one of the reasons that more experienced fleet drivers are over-represented in crashes is due to a desire to seek excitement and danger whilst driving for work. Previous research has found that thrill-seeking is associated with increased rates of accident involvement, traffic offences and self-reported violations and errors amongst commuter drivers (Matthews et al., 1996; 1997). Number of hours driving Results showed that number of hours spent driving per week was positively correlated with enjoyment of driving (r = 0.14, p < 0.001), hazard monitoring (r = 0.15, p < 0.001), fatigue resistance (r = 0.18, p < 0.001) and negatively correlated with aggression (r = –0.15, p < 0.001). It appears that the more driving undertaken in a week, the more a company car driver reports driving enjoyment, looking out for hazards, resisting the negative influences of fatigue and responding in a less hostile manner to other road users. However, hours spent driving was also positively correlated with work-related risk (r = 0.24, p < 0.001), driver confidence (r = 0.09, p < 0.01) and reappraisal coping (r = 0.20, p < 0.001) indicating a greater tendency to take risks when under pressure, report higher levels of confidence in driving skills and adopt ineffective coping strategies. Work related risk The new factor to emerge as a result of the PCA – work-related risk – was found to be strongly correlated with aggression (r = 0.34, p < 0.001), thrill-seeking (r = 0.47, p < 0.001), confrontive coping (r = 0.40, p < 0.001) and emotion focused coping (r = 0.36, p < 0.001), and significantly lower levels of fatigue resistance (r = –0.39, p < 0.001) and task focus coping (r = – 0.30, p < 0.001). These results show that at-work drivers who believe that taking risks under pressure is an inevitable part of their job report greater levels of driver stress, employ ineffective coping strategies and are more vulnerable to fatigue. Socially desirable responding Impression management was found to be strongly correlated with driver confidence consistent with expectations (Lajunen et al., 1997). High scores for impression management were also associated with task focused coping (r = 0.37, p < 0.001) and negatively correlated with confrontive coping (r = –0.40, p < 0.001) and thrill-seeking (r = –0.44, p < 0.001) suggesting that impression management scores are associated with a ‘safer’ profile and more effective driver coping strategies supporting its use as a measure of socially desirable responding as part of the FDRI. Driver confidence (a refined measure of self deception from the original DSDS) was found to be strongly correlated with task focused coping, enjoyment of driving, hazard monitoring and fatigue resistance. Strong negative correlations were found
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between driver confidence and confrontive coping and emotion focused coping. In other words, more confident drivers are reporting their behaviour in a more socially desirable way as with impression management, but perhaps through self-deception mechanisms rather than the more deliberate deception practised by high impression management scorers. We then conducted more powerful statistical tests to determine whether drivers who exhibit socially desirable responding tendencies also report safer driving behaviours and coping strategies. Participants were grouped according to whether they scored low, medium or high for impression management and for driver confidence based on 33rd and 66th percentiles as cut-off points. Significant differences between groups were found for all factors in the expected direction. High impression management scorers display a consistently ‘safer’ profile compared with low scorers except driver confidence and avoidance coping, which were both significantly higher for high IM scorers. Similarly, high driver confidence scorers display a consistently ‘safer’ profile compared with low scorers. Full analyses and discussion is beyond the scope of this paper but will be carried out as part of the ongoing development of the FDRI. Violations Penalty points on a driver’s licence is an indication of behaviours that increase the chances of being involved in a crash and are a useful criteria for validating a self reported measure of driver behaviour. We investigated whether violation history measured in terms of number of penalty points on their driver’s licence was associated with the FDRI scales. As found in previous studies, the number of licence points correlated positively with thrill-seeking (r = 0.08, p < 0.01) and we also found a positive correlation with work-related risk (r = 0.16, p < 0.001). We explored this relationship further using ANOVA. Results revealed significant differences for work-related risk according to violation history. Drivers with seven or more penalty points reported higher levels of risk taking due to time pressure compared with drivers with fewer penalty points (F = 9.15, p < 0.001). These findings support the use of this factor as an important determinant of at-work road risk. The negative correlation found between impression management (r = –0.12, p < 0.001) and violation history could be explained by the tendency for high scorers not to report their penalty points in the same way as they seem to report more socially desirable driving behaviours. We explored this relationship further using ANOVA and found that drivers with a higher number of penalty points reported significantly lower levels of impression management compared with drivers with none or few. Crash involvement Using crash involvement frequency as a criterion value for validity purposes, we grouped participants according to crash history and used ANOVA to determine whether high and low accident involved drivers differed according to their FDRI scores. The results show that drivers with two or more crashes reported higher levels of aggression (F = 2.67, p < 0.05) compared with those with one or no accidents
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– consistent with previous research on commuter drivers. Drivers with three or more crashes reported higher levels of work-related risk (F = 5.73, p < 0.001) and reappraisal coping (F = 3.72, p < 0.001) compared with drivers with two crashes or less. It appears that with multiple accidents involving company cars, drivers report increased levels of aggression and take risks in response to pressure at work and the demands of driving. They also report looking on the bright side when the demands of driving are difficult, which appears to be an ineffective coping strategy in comparison to other more safety-enhancing strategies such as task-focused coping. Conclusions We find support for previous research using the DSI, DCQ and DSDS and provide further evidence for a specific company car driver behavioural style in response to the demands of driving for work. Factor analysis of the three instruments revealed that the factor structures remained largely similar to that of drivers who do not drive for work, and support their use for understanding fleet driver behaviour, stress and coping. The PCA helped to refine the instrument, allowing several items to be discarded across all scales to create the fleet driver risk index. The reversals of dislike of driving to form enjoyment of driving and fatigue proneness to form fatigue resistance suggest that company car driving is more similar to the kind of driving behaviour reported by professional drivers than commuter drivers (Gandolfi and Dorn, 2005; Garwood and Dorn, 2005). It makes intuitive sense that company car drivers report that driving is generally enjoyable, otherwise they would be unlikely to work in a capacity in which it is a large part of their working day and, like the police, company car drivers report a greater resilience to fatigue. A new factor emerged, work-related risk, which went on to show important relationships with other factors and driver behaviours. We found support for the hypothesis that company car drivers experience significant levels of driver stress and utilise ineffective strategies such as confrontive coping. The transactional analysis of aggression, thrill-seeking and work-related risk suggests the need to direct interventions both towards negative appraisals of other drivers and motivations to take risk, replacing confrontive coping with more safetyfocused coping skills. Indeed, training of self-management skills in coping appears to be an effective treatment for driver anger (Deffenbacher et al., 2002). Both impression management and driver confidence were found to be important determinants of more positive responses to other scales, confirming their utility for inclusion in assessing company car driver behaviour. Perhaps drivers who lack competence fail to recognise they are incompetent (Kruger and Dunning, 1999). It has been found that when individuals are aware that their self-assessments are being validated by external measures there is an improvement in accuracy (Mabe and West, 1982). It is also the case that self-assessment is a skill that needs to be developed (Falchikov and Boud, 1989). Further research will investigate the role of impression management and driver confidence in self-assessment of company car driver behaviour and how to moderate its effects.
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Our data suggest that educating drivers to manage the stress of driving for work should be targeted towards the specific vulnerabilities of the individual driver, using the FDRI as a self-assessment in the first instance. To achieve this, driver trainers must have sufficient competence and coaching skills to be able to judge whether a driver has achieved their training goals. Work at Cranfield is ongoing to develop professional short and long courses to develop driving instructors’ competencies in how to manage behavioural, emotional and attitudinal risk. References Adams-Guppy, J.R. and Guppy, A. (1995). ‘Speeding in relation to perceptions of risk, utility and driving style by British company drivers.’ Ergonomics, 38, 2525– 35. Bartlett, M.S. (1954). ‘A note on the multiplying factors for various chi square approximations.’ Journal of the Royal Statistical Society, 16 (Series B), 296–8. Broughton, J., Baughan, C.J., Pearce, L., Smith, L. and Buckle, G. (2003). WorkRelated Road Accidents. TRL report 582. Crowthorne: Transport Research Laboratory. Chapman, P., Roberts, K. and Underwood, G. (2000). ‘A study of the accidents and behaviours of company car drivers.’ In G. B. Grayson (ed.). Behavioural Research in Road Safety X. Crowthorne: Transport Research Laboratory. Cooper, M.D. (2000). ‘Towards a model of safety culture.’ Safety Science, 36, 111– 36. Deffenbacher, J.L., Filetti, L.B., Lynch, R.S., Dahlen, E.R. and Oetting, E.R. (2002). ‘Cognitive-behavioral treatment of high anger drivers’. Behaviour Research and Therapy, 40, 895–910. Dorn, L. (2005). ‘Professional driver training and driver stress: effects on simulated driving performance.’ In G. Underwood (ed.), Traffic and Transport Psychology, Elsevier. Dorn, L. and Garwood, L. (2005). ‘Development of a psychometric measure of bus driver behaviour.’ Behavioural Research in Road Safety: 14th Seminar. Department for Transport, London. Dorn, L. and Matthews, G. (1995). ‘Prediction of mood and risk appraisals from trait measures: two studies of simulated driving.’ European Journal of Personality, 9(1), 25–42. Downs, C.G., Keigan, M., Maycock, G. and Grayson, G.B. (1999). The Safety of Fleet Car Drivers: A Review. TRL Report 390. Crowthorne, Berkshire: Transport Research Laboratory. Falchikov, N. and Boud, D. (1989). ‘Student self-assessment in higher education: a meta-analysis.’ Review of Educational Research, 59(4), 395–430. Field, A. (2000). Discovering Statistics Using SPSS (2nd ed.), London: Sage. Flin, R., Mearns, K., O’ Connor, P. and Bryden, R. (2000). ‘Measuring safety climate: identifying the common features.’ Safety Science, 34, 177–92. Gandolfi, J. and Dorn, L. (2005). ‘Development of the Police Driver Risk Index.’ in
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L. Dorn (ed.), Driver Behaviour and Training, vol. II. Aldershot: Ashgate. Garwood, L. and Dorn, L. (2005). ‘Stress vulnerability and choice of coping strategies in UK bus drivers.’ In L. Dorn (ed.), Driver Behaviour and Training, vol. II. Aldershot: Ashgate. Glendon, A.I., Dorn, L., Matthews, G., Gulian, E., Davies, D.R. and Debney, L.M. (1993). ‘Reliability of the Driver Behaviour Inventory.’ Ergonomics, 36, 719– 26. Gregersen, N.P., Brehmer, B. and Moren, B. (1996). ‘Road safety improvement in large companies: An experimental comparison of different measures.’ Accident Analysis and Prevention, 28, 3, 297–306. Griffiths, M. (1997). ‘Selecting safe vehicles: issues in vehicle crash safety.’ In Staysafe 36: Drivers as workers, vehicles as workplaces: Issues in fleet management. Report No. 9/51. Ninth report of the Joint Standing Committee on Road Safety of the 51st Parliament. Sydney: Parliament of New South Wales. Guadagnoli, E. and Velicer, W.F. (1988). ‘Relation of sample size to the stability of component patterns.’ Psychological Bulletin, 103(2), 265–75. Gulian, E., Matthews, G., Glendon, A.I., Davies, D.R. and Debney, L.M. (1989a). ‘Dimensions of driver stress.’ Ergonomics, 32, 585–602. Hatakka, M., Keskinen, E., Gregersen, N.P., Glad, A. and Hernetkoski, K. (2002). ‘From control of the vehicle to personal self-control; broadening the perspectives to driver education.’ Transportation Research Part F, 5, 201–15. Haworth, N., Tingvall, C. and Kowadlo, N. (2000). Review of Best Practice Road Safety Initiatives in the Corporate and/or Business Environment. Report No. 166. Accident Research Centre, Monash University, Victoria. Horne, J.A. and Reyner, L.A. (1995). ‘Sleep related vehicle accidents.’ British Medical Journal, 310, 565–7. Kaiser, H. (1970). ‘A second generation Little Jiffy.’ Psychometrika, 35, 401–415. Kline, P. (1994). An Easy Guide to Factor Analysis, London: Routledge. Kruger, J. and Dunning, D. (1999). ‘Unskilled and unaware of it: how difficulties in recognizing one’s own incompetence lead to inflated self assessments.’ Journal of Personality and Social Psychology, 77(2), 221–32. Lajunen, T. Corry A, Summala, H. and Hartley, L. (1997). ‘Impression management and self-deception in traffic behaviour inventories.’ Personality and Individual Differences, 22(3) 341–53(13). Lancaster, R. and Ward, R. (2002). The Contribution of Individual Factors to Driving Behaviour: Implications for Managing Work-Related Road Safety. Research Report. HSE Contract Research Report: HSE Books. Mabe, P.A. and West, S.G. (1982). ‘Validity of self-evaluation of ability: A review and meta-analysis.’ Journal of Applied Psychology, 67, 280–96. Matthews, G. (2001). ‘A transactional model of driver stress.’ In P.A. Hancock and P.A. Desmond (eds), Stress, Workload and Fatigue, Mahwah, NJ: Lawrence Erlbaum. Matthews, G. (2002). ‘Towards a transactional ergonomics for driver stress and fatigue.’ Theoretical Issues in Ergonomics Science, 3, 195–211. Matthews, G., Dorn, L., Hoyes, T.W., Davies, D.R., Glendon, A.I., and Taylor, R.G. (1998), ‘Driver stress and performance on a driving simulator.’ Human Factors,
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40, 136–49. Matthews, G., Emo, K.A. and Funke G.J. (2005). ‘The transactional model of driver stress and fatigue and its implications for driver training.’ In L. Dorn (ed.), Driver Behaviour and Training, vol. II, Aldershot: Ashgate. Matthews, G., Desmond, P.A., Joyner, L.A., Carcary, B. and Gilliland, K. (1996). ‘Validation of the Driver Stress Inventory and Driver Coping Questionnaire.’ Paper presented at the International Conference on Traffic and Transport Psychology, Spain. Matthews, G., Desmond, P.A., Joyner, L.A., Carcary, B. and Gilliland, K. (1997). ‘A comprehensive questionnaire measure of driver stress and affect.’ In T. Rothengatter and E. Carbonell Vaya, (eds), Traffic and Transport Psychology: Theory and Application, Amsterdam: Pergamon. Matthews, G., Dorn, L. and Glendon, A.I. (1991). ‘Personality correlates of driver stress.’ Personality and Individual Differences, 12, 6, 535–49. Matthews, G., Tsuda, A., Xin, G. and Ozeki, Y. (1999). ‘Individual differences in driver stress vulnerability in a Japanese sample.’ Ergonomics, 42(3), 401–15. Maycock, G. (1996). ‘Sleepiness and driving: the experience of UK car drivers.’ Accident Analysis and Prevention, 29(4), 453–62. Mitchell, R., Driscoll, T. and Healey, S. (2004). ‘Work-related road fatalities in Australia.’ Accident Analysis and Prevention, 36, 851–60. Pallant, J. (2001). SPSS Survival Manual, Maidenhead: Open University Press. Parker, D., West, R., Stradling, S. and Manstead, A.S.R. (1995). ‘Driving errors, driving violations and accident involvement.’ Ergonomics, 38, 1036–48. Shaffer, J.P. and Sinnett, E.R. (1964). ‘A factor analysis of Hall and Lindsey’s ratings of personality theories.’ Acta Psychologica, 22, 135–44. Shrauger, S.J. and Osberg, T.M. (1981). ‘The relative accuracy of self-prediction and judgement by others in psychological assessment.’ Psychological Bulletin, 90, 322–51. Stradling, S.G. (2000). ‘Driving as part of your work may damage your health.’ In G.B. Grayson (ed.), Behavioural Research in Road Safety IX. Crowthorne, Berkshire: Transport Research Laboratory, 1–9. Sundstrom, A. (2005). ‘Self-assessment of knowledge and abilities: a literature review.’ Report No. 54, UMEA Universitet, Sweden. Tabachnick, B.G. and Fidell, L.S. (1996). Using Multivariate Statistics (4th ed.), MA: Pearson Education. Vissers, J., Mesken, J., Roelofs, E. and Claesen, R. (2007). ‘New elements in the Dutch practical driving test: a pilot study.’ In L. Dorn (ed.), Driver Behaviour and Training, vol. III. Aldershot: Ashgate.
PART 4 Technological Interventions, Driver Behaviour and Road Safety
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Chapter 21
Development of Multimedia Tests for Responsive Driving Erik Roelofs,1 Marieke van Onna,1 Reinoud Nägele,2 Jolieke Mesken,2 Maria Kuiken2 and Esther Cozijnsen2 1 Cito, The Netherlands 2 DHV Environment and Transportation, The Netherlands Introduction Throughout Europe, increased attention is being given to road safety issues. Policy makers are looking for new ways to address road fatalities, in particular amongst novice drivers. Like in other countries, Dutch traffic safety policy is aimed at fostering safe traffic participation by means of stating traffic rules, maintaining the law, traffic education and communicative campaigns (Vissers, 2004). In various European countries a two-phased driver training programme, including a pre-licence and post-licence phase, is now under consideration. In addition, initial driver training programmes are being further improved by using professional instructional lesson designs aimed at systematic teaching of driving tasks. On top of that, initiatives for permanent traffic education have been developed. Traffic education is considered to be a life-long process. These developments are influenced by a changing conception of driving and driver training. Until recently the driver’s task was conceived of as a set of elementary driving tasks pertaining to vehicle control and applying traffic rules. Nowadays driving is considered as a broad domain of competence, in which the driver is expected to make independent decisions, taking into account the task environment, his or her interests and those of other traffic participants. A recently issued broad taxonomy of driving competence is known as the GDE matrix (Goals for Driver Education). It is used for developing curricula for driver training at both pre- and post-licence stages. The matrix stresses the overriding significance of the higher levels of driver behaviour with regard to accidents and the need for drivers to possess not only knowledge and skills, but also risk awareness and self-evaluation capacities at multiple levels (Hatakka, Keskinen, Gregersen, Glad and Hernetkoski, 2002; Hatakka et al., 2003). This shift towards competence-based learning induces new forms of driving assessment, as is the case for many forms of (vocational) education (Dierick and Dochy, 2001). Until recently, congruent with traditional views on driver training, assessment instruments took the form of rather isolated testing of knowledge about traffic rules in theory tests and driving skills in predominantly examiner-led road tests. As in other countries, in the Netherlands it is increasingly advocated to additionally assess higher order aspects of driving as represented in the GDE-matrix:
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risk tolerance; reflection on own driving behaviour; and hazard perception (Vissers, 2004). In 2007, Cito, a leading Dutch testing and assessment company, formed a consortium with several other Dutch organisations: consultancy and engineering group DHV, specialised in sustainable environmental planning, including transport; the Foundation for Scientific Research of Traffic Safety (SWOV); and TNO, a unit for Human Factors, whose mission is to apply scientific knowledge with the aim of strengthening the innovative power of industry and government. This consortium, named Cito Drive, is aimed at developing instruments for the assessment of driving competence in initial and post-licence driver training. The current chapter discusses the development of computer-based tests to assess the competence of drivers in perceiving and detecting relevant cues in a dynamic driving environment and in selecting an adequate response to changes in the complex task environment. In the next section we discuss some theoretical notions and research findings on essential cognitive aspects of the driving tasks. After that, a general model of competent performance is discussed and applied to the assessment of driving competence, followed by a section on the construction of the instruments for the assessment of responsive driving. The chapter closes with a short discussion. Perceiving the right cues and making the right decisions: change detection and action selection It can be expected that safe drivers are good at detecting and perceiving relevant cues in their environment at an early stage. Instead of continually responding to stimuli, a competent driver is able to anticipate situations. The degree to which a driver can operate in advance of real time events is a hallmark of experience and competence (Drummond, 1990). Driving is a complex task in a dynamic environment and it would be impossible to see and attend to all of the relevant cues in the environment while driving. The ability and capacity of drivers to attend to and consciously process information is limited. Through training and experience drivers apparently can develop ways to compensate for these cognitive limitations. Several very important dimensions can be distinguished (Drummond, 1990): •
•
•
Competent drivers develop more valid expectations about how events can turn out and become better in estimating the likelihood of occurrence of possible scenarios. Competent drivers develop optimal searching-strategies for relevant cues so they only need to recognise and process information that is essential for the driving task in specific situations. Competent drivers develop more efficient ways of processing information when they operate on a more automated level. Automation leads, for example, to chunking of relevant information into larger, more holistic and complex units and to refinement of the schematas or programmes that direct the task performance.
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Many research findings support the relationship between insufficient perceptualcognitive competences and the involvement in accidents. Underestimation of the complexity and demands of the task in specific situations (inadequate detection and perception skills) is often identified as a very important factor related to the accident proneness of young drivers (Engström et al., 2003, Joly et al., 1997; Gregersen, 1996; Gregersen and Bjurulf, 1996; Preusser, Ferguson and Williams, 1998; McGwin and Brown, 1999; Twisk, 1998a; 1998b; Summala, 1987). Differences in detection and perception skills between novice and experienced drivers are also reported. Experienced drivers are better and faster in detecting relevant cues than novice drivers (McKenna and Crick, 1991; 1994). Results of different studies show that with experience, drivers are more able to integrate information quickly and consider hazardousness as a holistic attribute of the driving environment (Deery, 1999). Other research has shown that search strategies of experienced drivers were found to be more flexible and they had a wider horizontal search pattern (Falkmer and Gregersen, in Engström et al., 2003). Novice drivers also fixate more on stationary objects, whereas experienced drivers fixate more on moving objects (Mayhew and Simpson, 1996). Novice drivers tend to be less flexible in their scanning behaviour and have a stronger fixation on cues that are irrelevant in these specific complex situations (Chapman, Underwood and Roberts, in Engström et al., 2003). In sum, research suggests that novice drivers perceive task situations less holistically and detect relevant cues less quickly and efficiently than experienced drivers. This seems to stem, at least in part, from novice drivers adopting less efficient information gathering strategies (Deery, 1999). Groeger’s four facet model of driving behaviour There are many models of driving behaviour that address the role of cognitive competence that underlie driver performance and responding to changes in a complex task environment. Most of them describe or explain the driving task from one perspective: either from a perspective of risk or threat, from a performance perspective or from a social psychological perspective. In a recently developed and validated model of driving behaviour, an attempt is made to integrate these factors (Mesken, 2006). Groeger identifies in his model (see Figure 21.1) four main facets in describing the cognitive processes that are involved in driving and in responding to changes in driving situations (Groeger, 2000; Grayson et al., 2003). The four facets explain how drivers adapt to changes in the task environment: an event may happen that implies an interruption of the drivers’ goals, the driver evaluates whether this event has consequences for the future and for future goals, and depending on that, he decides on action planning and implementation (Mesken, 2006, 31).
The first process, implied goal interruption, detects changes which imply some discontinuity in currently active goals. In other words, to start the process of a response to a change, some sort of discrepancy must be detected between the environment as is and the expected environment. Discrepancies are especially relevant when they are predictive of being endangered (Grayson et al., 2003). Originally this facet was seen
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in the context of hazard detection, but Groeger states that implied goal interruption reflects processes ‘that are also more generally involved in aspects of driving where other goals and expectations might be threatened’ (Groeger, 2000, 192). This process is described in more detail later as situation awareness. Environment
IMPLIED GOAL INTERRUPTION
APPRAISAL OF FUTURE INTERRUPTION
ACTION PLANNING
IMPLEMENTATION
Hypothetical Forward links Hypothetical Feedback links
Figure 21.1 Processes involved in responding to interruptions Source: Groeger, 2000.
The second process, appraisal of future interruption, appraises or evaluates detected changes. This process may be affected by personality characteristics (danger seeking, impulsiveness), beliefs about one’s self, level of confidence (selfefficacy), feelings of control (self-assessment of ability), the inherent controllability of the interruption, the seriousness of the consequences and what this means for the individual, and the assessment of the likelihood of the consequences if no action is taken. The outcome of this process is to act or not to act (Grayson et al., 2003). The third process, action planning, selects and constructs the most appropriate form of action under the actual circumstances. During driving, most actions are routinely performed under the control of a hierarchy of goals. There is a discontinuity in the normal hierarchy of control when an interruption is detected and appraised as sufficiently serious to require a response. Action planning depends on various types of reaction speed (simple reaction time, choice reaction time and un-alerted reaction time), mental processing (information-processing speed, inspection time and intelligence) and attention selection and inhibition (allocation). The fourth process of Groegers’ model of driving behaviour, implementation, is responsible for the implementation of the selected actions. Individual differences in motor performance and reaction times are expected to influence the successful implementation.
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Situation awareness According to Groeger’s model the first process, implied goal interruption, consists of several elements. Those elements are judgement of traffic scenes, hazard perception, judgement of speed and trajectories, estimating time-to-arrival, and style of decision making. Clearly, these elements are related to perceiving relevant information, interpreting this information and predicting or projecting the implications of this information in the future. This suggests a close relation with the concept of situation awareness. The term ‘situation awareness’ comes from the aviation domain. Although the recognised need for situation awareness can be traced back to World War I, an increased interest in the construct began in the 1980s and accelerated through the 1990s (Endsley, 2000). The emphasis on situation awareness and its measurement originated in the fighter aircraft domain, but the use and application of the term has rapidly spread to other domains like medicine, space operations and also to the domain of driving (Endsley, 2000). Endsley (1995a; 1995b) defines situation awareness as ‘the perception of elements in the environment within a volume of time and space, the comprehension of their meaning, and the projection of their status in the near future’. Consequently, Endsley developed a model of situation awareness in human decision making. According to this model, situation awareness encompasses three levels of information processing: •
•
•
Perception: perceiving and attending to important cues or elements in the environment. On this level scanning plays a crucial role. Scanning the environment should be systematic and active and leads to selectively attending to relevant cues. The quality of scanning increases when a driver regularly shifts focus. An analysis of mistakes made by airplane pilots show that three-quarters of all situation awareness mistakes are made during the first stage of perceiving. Overlooking the relevant information is the most common mistake. Other mistakes include perceiving the wrong information, difficulty in detecting the information, the available information is insufficient and memory mistakes (Jones and Endsley, 1996).• Comprehension: combining, interpreting, storing and retaining information. The second level is concerned with the correct interpretation and integration of the perceived information, and determining their relevance to the person’s goals. This is mostly done based on experience with previous task situations that are very similar to the one encountered. A situation is recognised, compared with the ones that are stored in the long-term memory and the best matching situation is activated (template-matching). A common mistake is that this match is based only on one or two cues or variables. This leads to misinterpretation of the situation. Of all situation awareness mistakes made by pilots, 20 per cent are made during this stage (Jones and Endsley, 1996). Projection: anticipating future events and their implications. The third level is being ahead of what might happen and being prepared to operate in advance of real time events. Drivers who are able to anticipate the course of events have
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more time and space to take action themselves or leave more time and space for others to operate in case of critical escalation of a situation. To anticipate knowledge and insight is crucial. It is impossible to predict something you don’t know. The things we have no knowledge of are the most threatening, because we don’t see them coming or can’t imagine a potential scenario as being possible. Of all situation awareness mistakes made by pilots, five per cent are made during this stage (Jones and Endsley, 1996). Novice drivers base their predictions too much on a deterministic vision of the course of events in traffic situations and too little on a realistic vision (Harrison, in Vlakveld, 2005). Loss of situation awareness often results in poor performance and even causes devastating accidents (Jones and Endsley, 1996). Recent research sheds some light on the role experience has in the development of situation awareness (Whelan, Senserrick, Groeger, Triggs and Hosking, 2004). Novice drivers have less short-term remembrance of important details in the traffic environment and less recollection of the behaviour of other road users. Also, novice drivers were more easily distracted when trying to recall the position of other road-users. Training and testing of responsive driving The aspects of driving Cito Drive focuses on tests for responsive driving such as a change detection and action selection. Change detection can be learned and tested. For example, a short training programme for increased knowledge, scanning and anticipation has shown to improve the quality of search strategies and patterns. Drivers were informed about their typical pattern of visual search and the need for scanning multiple locations in the visual field for potential dangers. They found notable changes in search patterns for horizontal spread compared with a control group (Chapman, Underwood and Roberts, in Engström et al., 2003). Responding to changes, by selecting an appropriate action, is part of the action planning in Groeger’s (2000) model. Successful action planning can only take place if the detection of changes is successful. In addition, the comprehension, projection and evaluation of cues has to be successful in order to plan an appropriate action. Both change detection and response selection can be viewed as cognitive aspects of driving competence. Assessment of competence The concept of competence has gained much attention in the many fields of education: general, vocational, higher education and learning on the job. On the basis of a study of dozens of definitions of competence (for example, Bunk, 1994; Spencer and Spencer, 1993; Parry, 1996) Mulder (2001) comes to the following definition of competence: ‘competence is the ability of a person or organisation to achieve particular levels of performance’ (76). According to Mulder, the competencies of individuals consist of: (a) integrated action proficiencies, (b) which are made up of
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clusters of knowledge structures, (c) cognitive, interactive, emotional, and where necessary psychomotor skills, (d) and attitudes and values which are necessary for, (e) the performance of tasks, (f) solving of problems, (g) and more generally the ability to function in a particular occupation, (h) a particular organisation, (i) a particular position, (j) or a particular role. In the cited literature on professional competence, it is emphasised that competence is not achieved per se, but must be kept up with changing requirements from society. The same holds for driving competence. The complex task of driving does not mean the same now as it meant 40 years ago, and will not bear the same meaning in the future. An eclectic model of driving competence It is widely accepted that theoretical models of task performance facilitate the construction of assessment instruments. These models can be taxonomic in nature or functional: the first describing domains of tasks and subtasks of the target domain; the latter describing the psychological processes underlying task performance. Theoretical models enable assessors to set up arguments to arrive at sound decisions about candidates, starting from observations of performance (Kane, 1992). As Kane (1992) states on valid assessment: it is not so much the test score itself that is valid or reliable, but rather the interpretations of test scores that can be more or less valid. The psychological theory on driving behaviour is explained in the previous section. The construction of assessment instruments of cognitive aspects of driving competence will be based on the general model of competent performance developed by Roelofs and Sanders (2003; 2007). The model is now serving the construction of various instruments for professional competence, including driving performance (cf. Nägele, Vissers and Roelofs, 2006). The model for assessment of driver competence (Figure 21.2) consists of five parts, the first being the driver’s task environment, consisting of strongly differing and specific task situations in which the other parts are embedded. The other parts are: Decision making, the process that involves action planning, selection and (mental) construction of the most appropriate form of action in the circumstances; the Actions that are actually carried out in specific situations; Consequences of the actions for other people, materials, or processes within a situation; and Base, which comprises the knowledge, skills, and attitudes of a candidate. The assessment of the change detection and action selection aspects of driving competence can be classified as assessments of the decision making process. Aspects of assessment of driver competence In driver competence assessment, the first question to answer is for what purpose drivers are assessed. Depending on the answer of the purpose question, different directions can be chosen for the content and type of assessment instrument. A first purpose is supporting driver learning. This can relate to candidates who learn to drive different types of vehicles (motorcycle, car, bus, trucks). The assessments will usually involve informal observations of actual driving behaviour resulting
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Drivers’ task environment
Base • • • •
Knowledge Skills Conceptions – Attitudes Personality characteristics
Actions Consequences
Decision making
Figure 21.2 An eclectic model for the assessment of driving competence Source: Based on Roelofs and Sanders, 2003; 2007. in immediate feedback and support for the pupils. As in school situations, many types of teacher-made assessments can be part of driver training. The results of these assessments can be used for adapting driving instruction to the pupils’ level of performance. A second purpose is to make high-stakes decisions about drivers, like licensing. A third purpose is scientific in nature, acquiring knowledge about the nature of the driving task and all kinds of variables influencing driving performance. A final purpose is to monitor and evaluate traffic policy. Not only the purpose of a test can vary, but also the methods for assessing professional competence may vary. They can be distinguished along different dimensions (Kane, 1992; Roelofs and Sanders, 2003): •
•
•
Authenticity of the assessment situation and task structure. The assessment can take place in the real task environment, in simulated or in symbolised situations. In simulated situations, aspects of the natural traffic situation are manipulated or omitted, as in the case of driving simulators. When driving competence is measured in symbolised traffic situations, a candidate is presented with a hypothetical situation, with a specific accompanying question or assignment. Type of evaluative data collection. The assessment data yielded in various situations can be statistics that are free of subjective judgment, (self) observations using diaries, logs or observation forms, or judgmental data. Actors involved in the data collection and judgment. In all types of data collection the drivers themselves are involved anyway. Other actors who score and judge the quality of the task performance may be educators, colleagues, external assessors or research scholars. In addition to human actors, ICT can also function as an assessor, although it uses inserted man-made performance standards.
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Research questions The current paper discusses the development of computer-based tests (CBTs) to assess important aspects of cognitive competence that underlie driver performance involving responding to changes in a complex task environment. Two CBTs will be developed: one is designed to assess a driver’s perception and detection of relevant cues for changes (change detection) and the other is designed to assess a driver’s competence to select an adequate response to changes in a complex task environment (action selection). Both change detection and response selection are viewed as important cognitive aspects of driver competence and are related to two out of four facets of Groeger’s model of driving behaviour (‘implied goal interruption’ and ‘action planning’). The tests are developed to support training of novice and experienced drivers. The results of the assessments can be used for adapting driving instruction to the pupils’ level of performance or to advise experienced drivers about specific types of additional training. The cognitive aspects of driver competence are assessed in symbolised traffic situations (a video). A driver is presented with digitally recorded situations accompanied by a question. The tasks are highly explicit: whether they detected changes in given situations and how the candidate would handle certain tasks in a given situation. They have to give a selective response. The assessment is free of subjective judgment; the evaluation is carried out automatically by means of performance standards. The argument to choose computer-based tests is that it is expected to be impossible to develop an on-road assessment instrument that covers enough critical situations for solid conclusions and at the same time avoids dangerous situations. Using CBTs, it is expected that this combination is a possible option. This leads to the following research questions: 1. Is it possible to construct computer-based tests for situation attentiveness (perception and detection of relevant cues) and action selection that cover a wide range of competence levels? Can the tests discriminate between pupils preparing for their driving licence and experienced drivers? 2. In which way are scores on the CBTs related to external criteria, for example to road assessments of defensive driving and driving simulator assessments of defensive driving? We expect that inexperienced drivers will perform more poorly on the tests of situation attentiveness and action selection than experienced drivers. In addition, we expect the correlations between the two CBTs will be substantial. More specifically, a hierarchical relationship is expected between the performance measures used. Good performance on the situation attentive test is necessary for good performance on the action selection test. Subsequently, good performance on the action selection test is necessary for good performance during on-road and in simulator performance.
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Construction of instruments The development of a test is usually a multi-phase procedure. First, a trial version is developed and a small pilot study is carried out to ensure that candidates understand the instructions and show the intended test behaviour. If the pilot results are far from desirable, a new trial version may be developed and tested before proceeding to the next construction phase. After the pilot study, a large-scale study is carried out. In this study, the potential test is administered to the target group. The final test is specified after analysing the results of the large-scale study. Before publishing the final test, a manual also has to be written and the results of the large-scale study have to be documented. The present test development project is still in the first indicated construction phase. In this phase, the adjustment of the test to the test purpose, the intended assessment methods and the theoretical framework is central. Test material Both Cito Drive responsive driving tests will be computer based. That is, the items are presented on a monitor screen and the candidate has to select responses with a mouse click. In both tests, a stimulus consists of a short movie, lasting between 5 and 15 seconds. It is displayed on a monitor and shows what a driver sees when looking through the front windshield. The rear-view mirror view is displayed in a small area centred in the upper area of the windshield view. The response format of both tests differs, resulting in the assessment of two different aspects of driving competence. The CBTs start with an instruction and trial items, to enable the candidate to practice with the item format. After the instruction, a candidate has to respond to 40 items. Including instruction and trial items, each CBT takes 25 to 30 minutes. A response format for the change detection test which requires limited language capacities is to ask the candidate to indicate a spot where the most attention is needed at the moment that the movie freezes or stops. Another possibility is to ask the opinion on statements like ‘a car is driving behind me now’. When the movie freezes, the task in the action selection test is to indicate which driver behaviour would be most appropriate at this moment. The response options are restricted to ‘Brake’, ‘Accelerate’, ‘Move left’, ‘Move right’, ‘Slow down gently’ and ‘Continue as before’. Item selection Two main options for item selection in the final tests are possible: a fixed format or a computerised adaptive test format (see for example Sands, Waters and McBride, 1997). The fixed format is most common: all candidates have to respond to the same item set, or several equivalent sets are available. In a computerised adaptive test, the presented items are selected during the test after a small number of fixed items, depending on the responses of the candidate to the previous items. This allows for adaptation of the difficulty of the items to the level of the candidate.
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A wide range of traffic situations has to be covered in each CBT. The situations which are filmed for the items are classified in five different traffic environments or road types (SWOV, 2006). 1. 2. 3. 4. 5.
urban access road in residential areas (30 km/h zones) urban distributor road (50 km/h) rural access road in residential areas (60 km/h zones) rural distributor road (80 km/h) through-road (100 / 120 km/h)
The main function of environments 1 and 3 is living. Relatively high numbers of pedestrians and bicycles can be expected. The function of environments 2 and 4 is mainly transport. Environment 5 comprises the motorways and regional throughroads. For each environment, multiple item-stems have to be filmed. In each final CBT, all environments have to be presented several times to cover the entire range of possible traffic situations. Finally, the item-stems or stimuli have to be filmed in the country where the final test is published. In this project, this means that the stimuli will be filmed in the Netherlands. This requirement ensures that the candidate is familiar with the presented traffic situations. Also, the camera car has to obey the local traffic rules, in order to avoid confusing the candidates. Participants The CBTs are intended for two main populations of candidates: pupil drivers and experienced drivers. The pupil driver population consists of pupils who vary in the amount of training they have had. The least experienced pupils have recently started driving instruction, mastering barely the technical aspects of car driving. The most experienced pupils are measured on the day they pass the driving test. The experienced driver population consists of lease car drivers, among others. Lease car drivers usually have several years of driving experience and they have a large annual mileage. These experienced drivers may be invited to participate in a post-licence training by the lease company. Statistical analysis All items in the CBTs have one response which is considered ‘correct’ and all other responses are considered ‘incorrect’. The one-parameter logistic model (OPLM, Verhelst and Glas, 1995), which is one of the item response theory (IRT) models, can be used to estimate the competence levels of all participants and the item parameters. IRT models have several advantages over more traditional analysis methods, like classical test theory. IRT models are probabilistic models, separating the observed responses from the underlying latent trait or competence level. For each candidate, the competence level can be estimated and the difficulty level of each item can be
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estimated. Also, missing data do not complicate the estimation process and IRT models allow for easy construction of parallel tests. Finally, computerised adaptive testing is not possible without IRT models. Discussion Responsive driving is an element of driving performance which is especially relevant for driver training and examination. Young novice drivers are slower in perception of changes than more experienced drivers, and their response selection is often inadequate (Grayson et al., 2003). Several studies have shown that experienced drivers are also more competent in detecting hazards and that adequate perception of complex, hazardous situations can be trained (McKenna and Crick, 1991; 1994; 1997). However, hazards are uncommon and thus the detection of them might not be based on training or on driving experience (Groeger, 2000). Improvement of hazard perception may ‘be a result of learning to identify situations in combination with automation of other driving tasks, thus reducing the mental workload and leaving more mental capacity for the hazard detection task’ (Engström et al., 2003, 33). The focus in the development of driver assessment instruments in the Cito Drive project is not explicitly on hazards, but supports Groeger when he states that implied goal interruption reflects processes ‘that are also more generally involved in aspects of driving where other goals and expectations might be threatened’ (Groeger, 2000, 192). Therefore, the competence assessment which is developed in this project will be based on the cognitive abilities that underlie driver performance in accomplishing multiple driver goals. In particular, we focus on the competence of detecting and responding to changes that imply some discontinuity in currently active goals. References Bunk, G.P. (1994). ‘Competentie-ontwikkeling in de Duitse beroepsopleidingen.’ [Development of competence in German vocational education]. Beroepsopleiding, 1, 8–15. Deery, H.A. (1999). ‘Hazard and risk perception among young novice drivers.’ Journal of Safety Research, 30(4), 225–35. Dierick, S. and Dochy, F. (2001). ‘New lines in edumetrics: new forms of assessment lead to new assessment criteria.’ Studies in Educational Evaluation, 27(4), 307– 29 Drummond, A.E. (1990). ‘An overview of novice driver performance issues: a literature review.’ Monash University Accident Research Centre. Endsley, M.R. (1995a). ‘Measurement of situation awareness in dynamic systems.’ Human Factors, 37(1), 65–84. Endsley, M.R. (1995b). ‘Towards a theory of situation awareness in dynamic systems.’ Human Factors, 37(1), 32–64. Endsley, M.R. (2000). ‘Theoretical underpinnings of situation awareness: a critical review.’ In M.R. Endsley and D.J. Garland (eds). Situation Awareness Analysis
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and Measurement. Mahwah, NJ: Lawrence Erlbaum Associates. Engström, I., Gregersen, N.P., Hernetkoski, K., Keskinen, E. and Nyberg, A. (2003). ‘Young novice driver education and training.’ Literature review, VTI-rapport 491A., Swedish National Road and Transport Research Institute, Linköping. Grayson, G.B., Groeger, J.A., Maycock, G., Hammond, S.M. and Field, D.T. (2003). Risk, Hazard Perception and Perceived Control. Report TRL560. Transport Research Laboratory TRL, Crowthorne. Gregersen, N.P. (1996). ‘Young drivers’ overestimation of their own skill – an experiment on the relation between training strategy and skill.’ Accident Analysis and Prevention 28, 243–50. Gregersen, N.P. and Bjurulf, P. (1996). ‘Young novice drivers: towards a model of their accident involvement.’ Accident Analysis and Prevention 28, 229–41. Groeger, J.A. (2000). Understanding Driving: Applying Cognitive Psychology to a Complex Everyday Task. Routledge: Psychology Press. Hatakka, M., Keskinen, E., Baugahn, C., Goldenbeld, Ch., Gregersen, N.P., Groot, H., Siegrist, S. Willmes-Lenz, G. and Winkelbauer, M. (2003). Basic Driver Training: New Models. Turku, University of Turku. Hatakka, M., Keskinen, E., Gregersen, N.P., Glad, A. and Hernetkoski, K. (2002). ‘From control of the vehicle to personal self-control: broadening the perspectives to driver education.’ Transportation Research Part F, 5, 201–15. Joly, P., Gilbert, M., Paquette, M., Perraton, F., Bergeron, J. (1997). ‘Performance in a driving simulator and intention to take risk on the road among learner and experienced young drivers.’ In Brookhuis, De Waard and Weikert (eds), Simulators and Traffic Psychology. HFES Europe Chapter. Jones, D.G. and Endsley, M.R. (1996). ‘Sources of situation awareness errors in aviation.’ Aviation, Space and Environmental Medicine, 67(6), 507–12. Kane, M.T. (1992). ‘An argument-based approach to validity.’ Psychological Bulletin, 112(3), 527–35. Mayhew, D.R. and Simpson, H.M. (1996). Effectiveness and Role of Driver Education and Training in a Graduated Licensing System. Traffic Injury Research Foundation, Ottawa. McGwin, G. and Brown, D.B. (1999). ‘Characteristics of traffic crashes among young, middle-aged and older drivers.’ Accident Analysis and Prevention, 31, 181–98. McKenna, F.P. and Crick, J. (1991). ‘Experience and expertise in hazard perception.’ In G.B. Grayson and J.F. Lester (eds), Behavioural Research in Road Safety. Crowthorne, TRL Limited. McKenna, F.P. and Crick, J. (1994). Hazard Perception in Drivers: A Methodology for Testing and Training. Report CR313. Crowthorne, TRL Limited. McKenna, F.P. and Crick, J. (1997). Developments in Hazard Perception. TRL Report 297. Crowthorne, TRL Limited. Mesken, J. (2006). Determinants and Consequences of Drivers’ Emotions. Dissertation Rijksuniversiteit Groningen. Mulder, M. (2001). Competentieontwikkeling in organisaties. Perspectieven en praktijk. [Development of Competence in Organisations. Perspectives and
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Practice]. The Hague: Elsevier bedrijfsinformatie. Nägele, R., Vissers, J. and Roelofs, E. (2006). Herziening WRM. Een model voor competentiegericht examineren [Revision of the Law for Driving Education for Motor Vehicles: A Model for Competence Based Certification Testing] Amersfoort/Arnhem: DHV/Cito. Parry, S.B. (1996). ‘The quest for competencies.’ Training, July, 48–56. Preusser, D.F., Ferguson, S.A. and Williams, A.F. (1998). ‘The effect of teenage passengers on the fatal crash risk of teenage drivers.’ Accident Analysis and Prevention, 30, 217–22. Roelofs, E.C. and Sanders, P. (2003). ‘Beoordeling van docentcompetenties.’ [Assessment of teacher competences]. In M. Mulder, R.Wesselink, H.Biemans, H.Nieuwenhuis and R. Poell (eds), Bevoegd, maar ook bekwaam? Groningen: Wolters-Noordhoff, 277–99. Roelofs, E.C. and Sanders, P. (2007). ‘Towards a framework for assessment of teacher competence.’ European Journal for Vocational Training (forthcoming). Sands, W.A., Waters, B.K. and McBride, J.R. (eds). (1997). Computerized Adaptive Testing. Washington, DC: American Psychological Association. Spencer, L.M. and Spencer, S.M. (1993). Competence at Work: Models for Superior Performance. New York: Wiley and Sons. Summala, H. (1987). ‘Young driver accidents: risk taking or failure of skills?’ Alcohol, Drugs and Driving, 3, nr. 3–4. SWOV Institute for Road Safety Research (2006). ‘Advancing sustainable safety: national road safety exploration for 2005–2020; the advanced vision in brief.’ http://www.swov.nl/rapport/DMDV/Advancing_Sustainable_Safety_brief.pdf; May 14, 2007. Twisk, D.A.M. (1998a). ‘Kansrijke maatregelen voor beginnende bestuurders. Eindrapport: uitgangspunten, effectiviteit en uitvoerbaarheid’ [Promising regulations for novice drivers. Final report: assumptions, effectivity and practical applicability]. Rapportnummer R-98-63. Leidschendam, SWOV. Twisk, D.A.M. (1998b). ‘Verkeersonveiligheid van jonge bestuurders in de periode 1985–1994’ [Traffic unsafety of the novice driver in the period 1985–1994]. Rapportnummer R-98-18. Leidschendam, Stichting Wetenschappelijk Onderzoek Verkeersveiligheid SWOV. Verhelst, N.D. and Glas, C.A.W. (1995). ‘The one-parameter logistic model.’ In G.H. Fischer and I.W. Molenaar (eds), Rasch Models: Their Foundations, Recent Developments and Applications. New York: Springer. Vissers, J. (2004). ‘Modernisering rijexamens, Probleemanalyse beginnende bestuurders’ [Modernizing driving certification tests, problem analysis of beginning drivers]. Report number TT04-021. Veenendaal: Traffic Test. Vlakveld, W. (2005). ‘Jonge beginnende automobilisten, hun ongevalsrisico en maatregelen om dit terug te dringen. Een literatuurstudie’ [Young novice car drivers, their accident risk and regulations to reduce this. A literature study]. Leidschendam, Stichting Wetenschappelijk Onderzoek Verkeersveiligheid SWOV. Whelan, M., Senserrick, T., Groeger, J., Triggs, T. and Hosking, S. (2004). Learner Driver Experience Project. Report No. 221, Victoria, Monash University Accident Research Centre.
Chapter 22
The Effect of Simulation Training on Novice Driver Accident Rates R. Wade Allen, George D. Park and Marcia L. Cook Systems Technology, Inc. Introduction The premise of this research project was that simulator training can reduce novice driver accident rates when compared with the accident rates of traditionally trained drivers. The simulator training included orientation and knowledge presentation and multiple exposures to simulator drives containing a high rate of hazardous and time critical encounters with traffic, pedestrians, signalised intersections, varying roadway configurations and traffic control devices (signs and markings). Driving scenarios were 12–15 minutes long and subjects had to complete six scenarios before being considered for graduation. Graduation was judged on a number of performance criteria (accidents, speeding violations, turn indicator usage, and so on) and subjects could meet the graduation criteria after six trials. They were given up to three additional trials to pass the graduation criteria. About 79 per cent of the subjects were able to meet the graduation criteria by the ninth trial. The overall training objectives were: (1) transmit knowledge relevant to driving (rules of the road, traffic control devices, hazard perception, and so on); (2) train situation awareness and hazard perception skills; (3) train decision making and appropriate control response skills under time pressure. Background Simulators with three levels of fidelity were used in this research. As illustrated in Figure 22.1 the simulator configurations included: (1) a desktop, single monitor or narrow field of view configuration (NFOVD); (2) a desktop, three monitor or wide field of view configuration (WFOVD); (3) an instrumented cab with projected wide field of view display (WFOVC). The first phase of this project was reported on by Allen, Rosenthal et al. (2003), and the detailed training results were reported by Allen, Cook and Park (2005). Data on the accident rates of our simulator trained subjects were obtained from the California State Department of Motor Vehicles (DMV). The analysis of this data is reported in this paper and compared with general teen accident rates reported elsewhere (Janke, Masten et al., 2003; Mayhew, Simpson and Pak, 2003).
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NFOVD: Single monitor, desktop; 45° field of view; 50% image size; sidewinder game controls; iconic side view mirrors; two school districts; students in driver education classes; one trained in computer lab; second trained in back of classroom. WFOVD: Three monitor, desktop; 135° field of view; 50% image size; sidewinder game controls; real image side-view mirrors; students recruited at local DMV office; trained in laboratory environment and supervised by researchers WFOVC: Three channel projected image; 135° field of view; 100% image size; instrumented cab; real image side-view mirrors; students recruited at local DMV office; trained in laboratory environment and supervised by researchers
Figure 22.1 Simulator configurations and deployment milieu
Previous studies have failed to show the efficacy of traditional driver education and training, although it still has popular appeal (Williams and Ferguson, 2004; Hedlund and Compton, 2005). For this reason many states in the US have instituted graduated driver licensing (GDL), which restricts novice driver exposure and the conditions under which they are trained. Studies have shown that GDL seems to reduce novice driver accident rates (Hedlund and Compton, 2005) and it is being advocated for general application. However, GDL does not directly influence the acquisition of skills required for safe driving, including situation awareness, hazard perception, decision making and psychomotor control, all of which must be applied under time pressure in critical situations in order to avoid accidents. In military training, simulation is accepted as an essential tool for teaching situation awareness, hazard perception, psychomotor skills and coping with malfunctions (Fletcher, 1998). Given that there is a significant concern for driver education (NTSB, 2003), it is relevant to consider the application of driving simulation to driver education. The US Center for Disease Control (CDC) has looked
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at the causes of injury and death for teenagers, and found that automobile crashes by far outweigh all other considerations as documented in NHTSA (2003). Based on this epidemiological finding, the CDC commissioned the research described herein to determine the efficacy of simulator training on novice driver accident rates. Methods Training system An automated training system was developed that would log in subjects, establish a database, administer orientation material, administer driving simulator scenarios, record driving performance, and compare subject data to criteria that would determine acceptable training performance (Allen, Guibert et al., 2006). After logging in subjects were administered orientation material that presented information necessary for driving the simulation, including: traffic control devices (signs, signals and markings), rules of the road, lane changing, turning and use of turn indicators, and hazard recognition, situation awareness and defensive driving. The orientation ended with information on the performance scoring system and use of the driving controls. Driving simulation The driving simulation has been described elsewhere (Allen, Rosenthal et al., 2002). The first exposure was a familiarisation run which slowly introduced the student to steering and speed control, then intersections with traffic control devices, and finally traffic and pedestrian conflicts. After the familiarisation run the training system presented the student with standardised training scenarios. The students were presented with six 12–15 minute training scenarios with performance scores displayed at the end of each run. Performance was evaluated at the end of the sixth scenario, and if the students met the performance criteria (for example, no accidents, no tickets and acceptable average speed) then they were graduated. If not, the application presented up to three additional trials. On any additional trial the students were graduated if they met the performance criteria. If the students drove all of the additional trials and did not meet the performance criteria, they were admonished to drive carefully in the future and acknowledged for their participation. Driving scenarios Driving scenarios were created with a scenario definition language (SDL) that allowed the specification and control of critical hazards (Park, Rosenthal and Aponso, 2004). The SDL allowed driving scenarios to be conveniently described in terms of roadway alignment, and include events for traffic control devices (signals, signs and markings), roadside objects, traffic and pedestrians. In addition the temporal properties of the traffic, traffic signals and pedestrians were triggered relative to the subject’s own vehicle in order to control the severity of hazard conflicts. The SDL also allowed
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situations to be counter balanced between scenarios so that subjects could not learn patterns of upcoming events over successive trials. The application was designed to select a different variation every time a given subject was administered an additional run. The characteristics of the driving scenarios, including critical events, have been described previously (Allen, Park and Cook, 2007). The SDL also allowed specification of performance measures. Performance measures included elements such as lane and speed deviations, speed limit and traffic signal violations, accidents, run completion time and median time to collision for all vehicle and pedestrian encounters. These performance measures were used as graduation criteria (that is, no accidents, no violations, and nominal values for lane and speed deviations and median time to collision). Scenario characteristics The driving scenarios were designed to be about 10–15 minutes in length depending on the driver’s speed. Participants drove along routes that covered distances of approximately 34 000 feet in length. At a speed of 45 mph the scenarios could be completed in about 8.5 minutes. There were sections where subjects could go faster, and sections where subjects had to slow or stop for intersections, traffic and pedestrians which extended the driving time to roughly 12–15 minutes. The scenarios involve 155 approaching vehicles and 107 interacting vehicles going in the same direction as the subjects. The vehicles were relatively evenly distributed throughout the scenarios. There were also 67 pedestrians distributed throughout the scenarios. The trigger times for pedestrians moving in front of the subject’s vehicle were designed to present challenging decisions to subjects (for example, typical time to encounter of three to six seconds). The signal light trigger timings were designed to give relatively critical stop or go decisions (for example, typical time to intersection of three to six seconds). Curve severity was designed to be challenging as subjects went faster than the general speed limit of 45 mph. Subject population and training sites Subjects trained in two research laboratories and two school districts as summarised in Figure 22.1. The research laboratory subjects were recruited from local Department of Motor Vehicle offices, while the high school district subjects were all registered in high school driver education classes. The population had a slight bias towards females, and there was some variation the total number of subjects driving each configuration as shown in Figure 22.2. Table 22.1 shows the participant distribution by simulator configuration and gender. The recruitment of subjects was limited by time and logistics and in each case we attempted to maximise the number of subjects.
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Number of participants
250 200 150 100 50 0
14
15
16
17
18
Females
35
126
81
56
19
Males
20
111
67 Age (years)
25
14
Figure 22.2 Subject population by age and gender Table 22.1 Subject population by simulator configuration and gender Simulator configuration
Recruiting method
Number of subjects
Males
Females
Wide FOV vehicle cab
DMV office
159
59
100
Wide FOV desktop
DMV office
180
74
106
Single monitor desktop
High school drivers ed.
215
104
111
554
237
317
Totals
Accident results Accident data for our student population were obtained from the state of California Department of Motor Vehicles. Figure 22.3 shows time distribution for training date to licensure date. The median subjects were generally receiving their licences about eight months from their training completion date. The distribution tails off significantly, however, so that some subjects were waiting two years or more before getting their licence. The wide field of view (WFOVC, WFOVD) groups have the tightest distributions, with the single monitor high school group (NFOVD) having a wider time range to licensure. Figure 22.4 shows the distribution of accidents over time. For total accidents across all training groups in Figure 22.4(a) we see that a significant number occur prior to licensure. After licensure the rate is high for the first three months, then falls off but the rate of occurrence is clearly sporadic. This is consistent with novice driver accident rates reported elsewhere (Mayhew, Simpson and Pak, 2003). The accidents after licensure are broken down by simulator training group in Figure 22.4(b), where we again see a significant rate in the first few months of driving. Beyond six months
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WFOVD
WFOVC
Number participants
40 NFOVD
30
20
10
0 -8
-2
4
10
16
22
28
34
40
Simulator training date to licensure date
Figure 22.3 Time distributions for subject licensure date Number of accidents
30 25 20 15 10 5 0 -36
-30
-24
-18
-12
-6 3 9 15 Months from licensure
21
27
33
39
a) Total accident count for 3 month intervals
Number of accidents
8 Wide FOV instrumented cab
Wide FOV desktop
Single monitor desktop
6 4 2 0 1
6
11
16
21
26
31
36
Months from licensure
b) Accident count for each simulator configuration
Figure 22.4 Accidents relative to licensure accident occurrence is somewhat sporadic which will require special analysis for comparative purposes. Figure 22.5 compares the accident rate (number of accidents per number of drivers) of the simulator training groups against previously published general novice
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driver accident rates over the first year of driving. The simulator trained subjects have lower accident rates (accidents per driver), however, there are problems with this comparison. The databases from the literature are fairly large and consistent over time. The accident database for our subjects has a dwindling number of subjects as illustrated in Figure 22.6, which shows the number of subjects in our subject population as a function of time beyond licensure. The subject population size falls off with time due to the length of time each subject waited to be licensed. Due to the decline in the subject population over time we computed accident rates on a monthly basis given the subject population at a given point in time. The 50 per cent point of each distribution as shown in Figure 22.6 was arbitrarily chosen as the limit of the length of time over which we will carry out this analysis. Figure 22.7 compares the cumulative accident rate of our simulator training groups with previously published data. The accident rates for the simulator trained subjects were calculated on a month by month basis by taking the accident that month shown in Figure 22.4(b) and dividing by the subject population size shown in Figure 22.6. The cumulative accident rates for the data from the literature (Janke, Masten et al., 2003; Mayhew, Simpson and Pak, 2003) were computed from their reported data. In Figure 22.7 we see that the wide FOV configurations show a lower accumulation rate of accidents than the previously published data, and the wide FOV and vehicle cab gave the lowest cumulative accident rates. Comparison of the cumulative accident rates in Figure 22.7 was carried out with linear regression analysis in which the intercept gives the initial accident rate and the slope gives the average accident rate beyond the initial period. The data are summarised in Table 22.2, which compares the intercepts, slopes and confidence intervals for each of the simulation configuration groups with general novice driver accident rates from the literature. Note that the accident rates from the two studies in the literature (Janke, Masten et al., 2003; Mayhew, Simpson and Pak, 2003) are quite comparable. The Janke data is from California, USA while the Mayhew data is
10
Accident rate (%)
8 6 4 2 0 WFOVD
WFOVC
NFOVD
Janke
Mayhew
Display configuration
Figure 22.5 Accident rates for each simulator configuration compared with previously published North American accident rates
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Number of subjects
250 WFOVC
200
WFOVD
NFOVD
150 50% 100
50%
50 50 0 0
10
20
30
40
50
Months since licensure
Figure 22.6 Number of subjects as a function of time beyond licensure
Accident rate (% accidents/driver)
30%
Mayhew
25%
Janke WFOVC
20%
WFOVD NFOVD
15%
10%
5%
0% 0
5
10
15
20
25
30
35
40
Months from licensure
Figure 22.7 Cumulative accident rate plots of simulator training groups as compared with rates from the literature Source: Janke, Masten et al., 2003; Mayhew, Simpson and Pak, 2003.
from Nova Scotia, Canada. The Mayhew data were reported on a monthly basis over the first two years of driving, while the Janke data were reported on a yearly basis over the first four years of driving. The slopes for the data from the literature give an accident rate of 0.0070 accidents per driver per month or 0.084 accidents per driver per year which is consistent with Janke’s reported first four year rates of 9.10, 8.88, 8.45 and 7.68 accidents per year per hundred drivers respectively (Table 3 in Janke, Masten et al., 2003).
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In Table 22.2 and Figure 22.8 the regression slope generally describes the accident rate beyond the initial few months. Here we see that the simulator configuration groups have lower regression slope accident rates than the general novice driver accident rates reported in the literature. The subject group trained with the wide FOV and vehicle cab configuration had an accident rate that was about one-third (34.3 per cent) of the general novice driver accident rates from the literature (Janke, Masten et al., 2003; Mayhew, Simpson and Pak 2003). The wide FOV desktop group had an accident rate that was 75 per cent of the accident rate from the literature. The single monitor group had an accident rate of 87.1 per cent of the rate reported in the literature. The confidence intervals in Table 22.2 show that the accident rate differences between the simulator training data sets are generally reliable. In Table 22.2 and Figure 22.8, the intercept of the regression function general describes the initial accident rate over the first few months. Here we see that the single monitor simulation group (NFOVD) had a significantly higher initial accident rate than all other data sets, and this result is reliable based on non-overlapping confidence intervals. This is also apparent in the cumulative accident rate data plotted in Figure 22.7. The narrow FOV group initial accident rate is over two times greater than the other data sets as indicated by the large intercept. This high initial accident rate is also evident in the accident counts shown in Figure 22.4. Discussion These results have implications for training simulator display configurations and more broadly for simulator fidelity. At a minimum it would appear that full sized projected displays are significantly superior in their training value to minified monitor presentations. The wide field of view also seems to be important, as the groups trained on the single monitor display only had some slight improvement in accident rate. Previous analysis of the training data has shown some downsides to Table 22.2 Accident rate regression analysis Data Set Mayhew Janke
R2 0.993 0.999
WFOV cab
0.918
WFOV desktop
0.986
NFOV desktop
0.968
Coefficients
P-value Lower 95% Upper 95%
Intercept
0.0164
0.000
0.0128
0.0199
Slope
0.0070
0.000
0.0067
0.0072
Intercept
0.0104
0.187
-0.0122
0.0329
Slope
0.0070
0.001
0.0063
0.0076
Intercept
0.0178
0.000
0.0130
0.0225
Slope
0.0024
0.000
0.0021
0.0026
Intercept
0.0080
0.001
0.0035
0.0124
Slope
0.0053
0.000
0.0051
0.0056
Intercept
0.0354
0.000
0.0283
0.0424
Slope
0.0061
0.000
0.0056
0.0066
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the single monitor training where drivers seem to drive faster than with the wide field of view displays which could relate to peripheral cues (Park, Allen et al., 2005). 0.008 Slope
Accident rate
0.006
0.004
0.002
0.000 WFOVC
WFOVD
NFOVD
Janke
Mayhew
Janke
Mayhew
Data Set
a) Slope
0.040 Intercept
Accident rate
0.030
0.020
0.010
0.000 WFOVC
WFOVD
NFOVD
Data set
b) Intercept
Figure 22.8 Cumulative accident rate regression analysis trends The value of the instrumented cab and surround is unknown at this time, and is a significant confounding variable. It would appear at a minimum that simulators for driver training should have wide aspect ratio displays scaled and positioned to give real world image sizing and including real image side view mirrors (not icons as presented by the single monitor desktop configuration herein). There are other confounding variables with each of the simulator training groups involving their selection, supervision and administration of training as listed in Figure 22.1. The simulator training with the higher fidelity configurations occurred in research laboratories and was administered by research staff, and subjects were recruited at
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local California Department of Motor Vehicle offices. The single monitor systems were deployed in high schools as part of their driver education classes. Students and teachers did report enthusiasm for the simulator training system. Conclusion The analysis presented herein shows that simulator training may lower teen driver accident rates. The analysis also shows that simulator fidelity may have a significant influence. The current results do not lead to a definite description of an appropriate simulator configuration, but near full size imagery is definitely indicated. Full size wide aspect ratio images may provide an affordable answer to this requirement and can be provided by flat panel and projected displays in the future. Acknowledgement This research was funding by the US Centers for Disease Control and Prevention under research grant number 5 R44 CE00111-03 entitled ‘A PC Based Low Cost Simulator for Driver’s Education’. References Allen, R.W., Cook, M.L. and Park, G.D. (2005). ‘Novice driver performance improvement with simulator training.’ Proceedings of the 2nd International Conference on Driver Behaviour and Training, Edinburgh, UK, 15–17 November. Allen, R.W., Guibert, M.R., et al. (2006). ‘A user configurable PC platform for driver assessment and training.’ Proceedings of the Driving Simulation Conference Asia/ Pacific, Tsukuba, Japan, May/June 2006. Allen, R.W., Park, G.D. and Cook, M.L. (2007). ‘The effect of display configuration on the training value of novice driver simulators.’ Proceedings of the Image 2007 Conference, Scottsdale, AZ, July. Allen, R.W., Rosenthal, T.J., et al. (2002). ‘A low cost platform for training and evaluating driver behaviour.’ Proceedings of the Driving Simulation Conference, DSC 2002, Paris, France, September. Allen, R.W., Rosenthal, T.J., et al. (2003). ‘Experience with a low cost, PC based system for young driver training.’ Proceedings of the 1st International Conference on Driver Behaviour and Training, Stratford-upon-Avon, UK, 10–12 November. Fletcher, J.D. (1998). ‘Measuring the cost, effectiveness, and value of simulation used for military training.’ Proceedings of the Simulation Industry Association of Australia, http://www.siaa.asn.au/get/2410061680.pdf. Hedlund, J. and Compton, R. (2005). ‘Graduated driver licensing research in 2004 and 2005.’ Journal of Safety Research, 36, 109–19. Ivancic, K. and Hesketh, B. (2000). ‘Learning from errors in a driving simulation:
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effects on driving skill and self-confidence.’ Ergonomics, 43(12), 1966–84. Janke, M.K., Masten, S.V., et al. (2003). ‘Teen and senior drivers.’ California Dept. of Motor Vehicles Report CAL-DMV-RSS-03-194, July. Joung, W. and Hesketh, B. (2006). ‘Using “war stories” to train for adaptive performance: is it better to learn from error or success.’ Applied Psychology: An International Review, 55(2), 282–302. Mayhew, D.R., Simpson, H.M. and Pak, A. (2003). ‘Changes in collision rates among novice drivers during the first months of driving.’ Accident Analysis and Prevention, (35), 683–91. National Transportation Safety Board (2003). Report of Proceedings, Public Forum on Driver Education and Training, October 28–29, 2003, NTSB/RP-05/01, PB2005-917003, Notation 633A. NHTSA (2003). ‘Traffic safety facts 2002 young drivers.’ NHTSA National Center for Statistics and Analysis, Washington, D.C., DOT HS 809 619, 2003. Park, G.D., Cook, M.L., et al. (2006). ‘Automated assessment and training of novice drivers.’ Advances in Transportation Studies an International Journal, 2006 Special Issue, December, 87–96, Aracne, Rome, Italy. Park, G., Rosenthal, T.J. and Aponso, B.L. (2004). ‘Developing driving scenarios for research, training and clinical applications.’ Advances in Transportation Studies an International Journal, 2004 Special Issue, December, 19–28, Aracne, Rome, Italy. Williams, A.F. and Ferguson, S.A. (2004). ‘Driver education renaissance?’ Injury Prevention, (10), 4–7.
Chapter 23
Driving Experience and Simulation of Accident Scenarios Catherine Berthelon, Claudine Nachtergaële and Isabelle Aillerie Institut National de Recherche sur les Transports et leur Sécurité, France Introduction Novice drivers have a strong probability of being involved in accidents, but the interactions between driving experience and age are hard to analyse. In fact, beginner drivers are often young and predisposed to taking risks, which can be seen in the fact that, for the same amount of time taken to earn their driving licence, beginners aged 20 years and over have fewer accidents than beginners under the age of 20 (Deery, 1999; McCarrt, Shabanova and Leaf, 2003; Williams, 2003). The probability of being involved in an accident is also connected to the driver’s gender, with an over-representation of young men (Williams, 2003; Monarrez-Espino, Hasselberg and Laflamme, 2006). The effect of experience may be linked to the high-level cognitive activities required in dealing with difficult situations (Ranney, 1994; Gregersen and Bjurulf, 1996; Fisher et al., 2002), but more elementary aspects of performance may also vary. Thus, a certain level of perceptive expertise is characterised by a better ability to foresee events from the context and to detect cues that are relevant to this prediction (Mourant and Rockwell, 1972; Crundall, Underwood and Chapman, 1999; Underwood et al., 2002; Underwood et al., 2003; Berthelon, Mestre and Taramino, 1995). Concerning driving skills themselves, while basic vehicle control and traffic rules can be learnt quite quickly (Hall and West, 1996), experience calls on the acquisition of complex coordination of motor and perceptive activities (Mac Leod, 1987), which can be seen in the fact that experienced drivers show more effective integration and coordination than beginner drivers (Salthouse and Somberg, 1982; Mayhew, Simpson and Pak, 2003). Thus, research carried out in this field suggests that the performances of beginner drivers are inferior to those of experienced drivers, which is corroborated by the fact that the strong probability of young people being involved in an accident drops as the time needed to obtain the driving licence increases (Turner, McClure and Pirozzo, 2004; Williams, 2003). To address the over-involvement of novice drivers in traffic accidents, France adopted a reform of the driver training system in 1988 with the possibility of taking Early Driver Training (AAC – Apprentissage Anticipé de la Conduite) at the age of 16. AAC is a step in the system for gradual access to driving. This driver training includes initial training in driving schools after which the level is assessed to be equivalent to those of young people who follow the traditional training to obtain
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their licence. The learner acquires additional experience consisting of driving with an adult for at least 3 000 km, along with educational meetings organised with the driving school. In any case, the driving test is taken no earlier than the age of 18. Although the success rate among those who choose the AAC when taking their driver test appears to have decreased since 1997, it is still much higher than for those who follow the traditional driver training approach. Drivers who follow this type of training could be less likely to be involved in accidents than drivers who follow the traditional training, and for whom the probability of an accident is high in the first months after receiving their licence (Mayhew et al., 2003; Sagberg and Bjørnskau, 2006). We shall focus part of the work presented here on the consequences that this training could have on driving behaviour itself. The objective is therefore to determine whether AAC favours the ability to perceive and foresee the behaviour of other users and enables the learner driver to develop behaviour that is closer to that of an experienced driver following the traditional driver training approach. Today simulators can be used to reproduce vehicle dynamics. Drivers have also been found to exhibit behaviours close to those observed in reality, due to the realism of the simulation. Simulators reproduce extreme conditions that would be difficult, or even impossible, to put into practice under normal conditions of traffic and safety. The methodology presented below aims to assess the relevance of introducing scenarios identified as generating accidents in the simulators and to assess whether the simulator discriminates between drivers according to experience and driver training method. Method Subjects The subjects are males who are not sensitive to travel sickness. The beginner drivers took traditional driver training (DCT; N = 12) or early driver training (DCA; N = 7). Their average age is, respectively, 19.6 years (δ = 1.4) and 18.4 years (δ = 0.5). They had passed their driver test within the last two months and do not have their own personal vehicle. The experienced drivers (EXP; N = 11) have an average age of 24.5 years (δ = 2.9) and have had their driving licence and a vehicle for more than three years. Experimental system and task The subjects sit at an interactive driving station comprising one quarter of a vehicle with a fixed base (Figure 23.1). The image projection surface underlies an angular opening that spans 58° horizontally and 49° vertically. The images are generated at a frequency of approximately 30 Hz.
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Figure 23.1. The pedestrian shoots out from the right 2.4 seconds before the driver crosses his trajectory Each subject undergoes three rounds of the circuit, lasting approximately seven minutes for each round. Some autonomous vehicles with non-conflicting behaviours are driving on the circuit. The first round is an opportunity to get used to the simulator commands. The scenarios are introduced randomly in the second and third rounds to avoid any order effect. The recommendation is to follow the road signs indicating the town centre and to drive at a speed of approximately 50 km/h. No indications are given suggesting that unexpected events will occur. Description of the scenarios and their implementation In road safety literature, the scenario concept concerns a group of accidents presenting similarities from the point of view of the chain of events and relations of causality in the different phases leading to the collision (Fleury and Brenac, 2001). The sequential analysis method used to group the accidents in the form of scenarios is based on a segmentation of their progression in several phases, thus providing a characterisation of the prototypical situations that generate accidents (Brenac and Fleury, 1999; Brenac, Nachtergaële and Reigner, 2003; Brenac et al., 1996), making it possible to undertake a selection for use in driving simulators. Once the selection is made, the scenarios are implemented with Detailed Accident Study data from INRETS/MA, which constitute reference situations. These highly precise data provide the description of the infrastructure and time positioning of the vehicles and/or pedestrians involved in a scenario (Ferrandez et al., 1995; Perrin et al., 2004). The scenarios implemented can also be introduced into the simulator by the Modelling, SImulation and driving Simulator team at INRETS (MSIS – Modélisations, SImulations et Simulateurs de conduite). The ‘hidden pedestrian crossing’ scenario corresponds to approximately 19 per cent in terms of the population involved in pedestrian bodily injury accidents in France (Brenac, Nachtergaële and Reigner, 2003). A vehicle is travelling in an urban area on a straight main through-road, generally not in an intersection. A pedestrian
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is getting ready to cross the road outside a pedestrian crossing. The pedestrian, initially hidden from the driver’s view, usually by a parked vehicle, starts to cross as the driver arrives. The driver does not detect the pedestrian or does so very late. The pedestrian does not see the vehicle. In our simulation, the pedestrian is hidden by a bus parked on the right-hand side of the carriageway; he suddenly crosses the carriageway in front of the bus as the driver is arriving from his left (Figure 23.1). The pedestrian appears in the driver’s field of vision 2.4 seconds before he reaches him. This configuration remains effective only if the driver does not take any manoeuvre to avoid him. The ‘sudden stop at a traffic light’ scenario corresponds to 36 accident cases (Brenac et al., 1996; Blancher, Brenac and Fleury, 1998). The driver is driving in a queue on a straight two-way carriageway. Vehicles and heavy trucks are driving ahead of their car, blocking the upcoming T-junction and the traffic light regulating the junction. This is the first traffic light on their itinerary. The light turns red, and the vehicles in front of the subject’s car brake at a distance of approximately 25m to stop at the light. The driver is taken by surprise and brakes too late. To implement this scenario, we have defined a queue of three vehicles, the first of which is a bus hiding the upcoming junction and traffic light. The speed of the queue depends on that of the subject, so that the distance between the subject and the last vehicle in the queue is 25 m over a distance of approximately 500 m. The bus, followed by the second vehicle, turns left at the junction. The vehicle preceding the subject brakes for 2.9 seconds to stop at the traffic light. Two other scenarios are directly inspired by the accident data available at INRETS/ MA. They correspond to situations that are less representative in accidentology but which are nonetheless typical of what all drivers encounter on the road. In the ‘vehicle overtaking and pulling back into the lane’ scenario, the subject is driving in the right-hand lane of a straight urban main road with three lanes, two lanes being allocated to their travel direction. A vehicle overtakes the subject; its speed is 10 km/h greater than theirs. When this vehicle is positioned 20 m in front of the subject, it starts to pull back in front of them while slowing down; three seconds later, it is completely back in the right-hand lane (therefore in front of the subject) and adopts a speed of 30 km/h. The ‘vehicle pulling out of parking’ scenario corresponds to the following situation: a vehicle parked on the right-hand side pulls out of its parking space as the subject has just turned left at an intersection and is speeding up. This manoeuvre is performed when the subject is 20 m away. Results The variables analysed concern the speed and lateral position of the drivers and their response time when confronted with the event. Inter-vehicle times (TIV) were recorded in all scenarios except for ‘hidden pedestrian crossing’. The variables are analysed using analysis of variance (ANOVA), given the large number the standard deviations cannot be illustrated. Only significant results are presented here.
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‘Hidden pedestrian crossing’ scenario Response times following the appearance of the pedestrian are significantly shorter for the EXPs than for the DCAs when releasing the accelerator pedal, but does not differ significantly for stepping on the brake pedal (see Table 23.1). Table 23.1
Hidden pedestrian crossing
Stop at a traffic light
Vehicle overtaking and pulling back in Vehicle pulling out of parking
Average response times as a function of driving experience and scenario; standard deviations in parentheses Accelerator (s) EXP DCT DCA N= N= N=5 11 12 0.79 0.64 0.7 (0.08) (0.1) (0.23) N= 11 1.09 (0.65) N=8 1.77 (0.36)
N= 12 1.06 (0.6) N= 10 1.58 (0.79)
N= 10 0.61 (0.21)
N= 12 1.32 (0.78)
N=3 1.03 (0.48) N=5 1.26 (0.41)
N=7 0.79 (0.33)
T EXP < DCA T= –2.77 p DCA T= 2.38 p < 0.04 EXP < DCT; T= –2.77 p < 0.01
EXP N= 11 0.98 (0.14)
Brake (s) DCT N= 12 1.11 (0.38)
T
N= 10 2.08 (1.28) N=6 2.83 (0.79)
N= 11 1.85 (0.81) N=9 2.51 (0.59)
N=7 2.09 (1.29)
NS
N=4 2.06 (0.54)
NS
N= 10 0.93 (0.24)
N=8 1.29 (0.40)
N=7 1.14 (0.34)
DCA N=7 0.96 (0.19)
NS
EXP < DCT T= –2.35 p < 0.03
Time influences speed (F[97,2619] = 165.65; p < 0.0001) and lateral positions (F[97,2522] = 5.75; p < 0.0001). Speeds start to drop 0.7 seconds after the pedestrian becomes visible. The decrease continues for 3.5 seconds, then the speed stabilises for one second and the subjects accelerate. Swerving toward the right of the carriageway becomes significant approximately three seconds after the pedestrian becomes visible. The swerving increases significantly for two seconds, then the lateral positions remain stable (see Figure 23.2). The interaction between the group and time (F[194,2522] = 1.72; p < 0.0001) shows that the swerve toward the right is due to the EXPs who start swerving two seconds after the pedestrian becomes visible. The swerving increases for 1.5 seconds and its maximum amplitude is 43 cm. The lateral position of DCTs and DCAs does not change significantly during progression (see Figure 23.3).
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*
** ***
16
lateral position
14 190
12 10
180
8 170
6 4
160
speed
Speed (m/s)
Lateral position (cm)
200
2 150
0 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7
Time(s)
Figure 23.2 Average speeds and lateral positions as a function of time ‘Sudden stop at a traffic light’ scenario The subjects’ response times do not vary significantly as a function of driving experience (Table 23.1). Speeds evolve over time (F[139,3753] = 55.74; p < 0.0001). They decrease significantly 0.5 seconds after the vehicle being followed brakes; the decrease continues to the end of the recording (see Figure 23.4). The lateral positions and the TIVs do not evolve significantly as a function of time. EXP
Lateral position (cm)
*
** ***
210
DCT
200
DCA
190 180 170 160 150 140 0 0.5 1 1.5 2 2.5 3
3.5 4 4.5 5 5.5 6 6.5 7
Time(s)
Figure 23.3 Average lateral positions as a function of time and driving experience
Driving Experience and Simulation of Accident Scenarios
lateral position
10
175 8
172
6
169 166
4
163
2
160
speed
Speed (m/s)
Lateral position (cm)
12 178
283
0 0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
Time(s)
Figure 23.4 Average speeds and lateral positions as a function of time ‘Vehicle overtaking and pulling back into the lane’ scenario The analyses for this scenario were performed on 24 subjects. Response times do not vary significantly as a function of driving experience (see Table 23.1). Speeds and lateral positions evolve as a function of time (F[139,2919] = 62.20; p < 0.0001 and F[139,2919] = 1.98; p < 0.0001, respectively). Speeds decrease significantly one second after the start of the manoeuvre of the adverse vehicle; the decrease lasts 4.5 seconds, then speeds stabilise. The subjects swerve towards the left 1.5 seconds after the vehicle is totally back in the lane (see Figure 23.5).
Lateral position (cm)
*
**
100 50
16
5 subjects
14
average
12
19 subjects
10
0
8 6
-50
4 -100
Speed (m/s)
150
speed
2
-150
0 0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
5.5
6
6.5
7
Time(s)
Figure 23.5 Average speeds and lateral positions as a function of time We can see that five subjects (three EXPs and two DCTs) swerved into the left lane when the other vehicle pulled into their lane in front of them (Figure 23.5). The TIV recording stopped prematurely for these five subjects. The TIV analysis was therefore performed on 19 subjects.
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4 3.5
TIV (s)
3 2.5
**
*
2 1.5 1 0.5 0 0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
Time(s)
Figure 23.6 Average TIVs as a function of time
12
3.5
* **
3.0 2.5
8
2.0 6 1.5 4
TIV (s)
Speed (m/s)
10
1.0
2
0.5
0
0.0 0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
speed TIV
5
Time(s)
Lateral position (cm)
Figure 23.7 Average speeds and TIVs as a function of time 60 70
left
80 90 100
EXP
110
DCT DCA
120 130
right
140 0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
Time(s)
Figure 23.8 Average lateral position as a function of time and driving experience
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TIVs evolve over time (F[99.1485] = 20.48; p < 0.0001). They decrease significantly 0.5 seconds after the vehicle starts to merge back into the right-hand lane, then stabilise until the vehicle is totally back in the lane (duration: 1.5 seconds), then increase for the last three seconds (see Figure 23.6). ‘Vehicle pulling out of parking’ scenario The DCTs take more time than the EXPs to release the accelerator. Response times for pressing the brake pedal do not vary significantly as a function of the group but certain subjects did not brake, notably three DCTs who hit the vehicle merging into their lane (see Table 23.1). Speeds and TIVs evolve significantly as the subjects progress (F[139.3753] = 38.26; p < 0.0001 and F[139.3197] = 21.55; p < 0.0001, respectively). A significant drop in speed occurs during the first second and lasts for 2.5 seconds; the speed stabilises for 0.5 seconds, then increases again. TIVs decrease 0.5 seconds after the vehicle starts to pull out of the parking space and for one second, then they stabilise for 0.5 seconds, increasing significantly for the remaining three seconds (see Figure 23.7). Lateral positions do not evolve over time but we can observe an interaction between the group and time on this variable (F[278.3753] = 3.91; p < 0.0001). DCTs are positioned significantly more to the right of their lane than EXPs for the first two seconds (23 cm difference). They swerve to the left of the lane 0.5 seconds after the other vehicle starts pulling out of the parking space; this swerve lasts one second, then their position stabilises. EXPs swerve to the right of the lane four seconds after the vehicle starts to pull out of the parking space. The lateral position of DCAs is stable (see Figure 23.8). Singular behaviours These behaviours can be attributed to five EXPs who often avoid conflict by swerving laterally and five DCTs who often cause collisions. We can observe an absence of singular behaviours amongst the DCAs (Table 23.2). Moreover, five of the six subjects who collide (four DCTs and one EXP) drive at high speeds and consequently have short TIVs, which reduces the possibility of avoiding collision simply by braking. The ‘vehicle pulling out of parking’ scenario leads to collision for three DCTs and the ‘vehicle overtaking and pulling back into the lane’ scenario is the only one for which no collision is observed, but it leads to more swerving into the left-hand lane (see Table 23.2). One DCT collides in two scenarios; he drives at high speed, leading to a TIV that is too short to avoid collision. One EXP systematically swerves laterally, with or without braking, in three scenarios out of four. He is the only driver to try and avoid the pedestrian to the left and by doing so collided with the pedestrian. We have therefore hypothesised that he was sensation seeking, amplified by driving in simulation. In a natural road environment, his behaviour would be assimilated with risk-taking understood in terms of excessive confidence in his own abilities or in terms of increased exposure
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Table 23.2 Singular behaviours
Hidden pedestrian crossing EXP EXP
Sudden stop at a traffic light
Vehicle overtaking and pulling back in
Vehicle pulling out of parking
C C
AR
Total
1C AL
EXP
1C + 2A AL
1A
EXP
AL
1A
EXP
AL
1A
Total EXP
1AL
2C + 5A
DCT
C
1C
DCT
C
1C
DCT
C
1C
1C
1C + 1AR
3AL
DCT
AL
1A
DCT
AL
1A
Total DCT
2AL
3C
3C + 2A
5A
3C + 1A
5C + 7A
Total
1C
1C + 1A
C = Collision; A = Avoidance, AL = Avoidance to the left; AR = Avoidance to the right due to the travel conditions (Summala, 1987). These data represent one of the limitations of simulator-based research. Discussion After selecting prototypical accident-generating situations or accident scenarios (Fleury and Brenac, 2001) using data from real accident cases, we recreated them for the driving simulator.
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Experienced and beginner drivers were asked to drive the simulator and their behaviour was analysed. Insofar as the origin of accidents has many factors, of course, a small percentage of the drivers actually collided in the situations presented. However, two scenarios clearly show the effect of experience on driving; the subjects are confronted with an unforeseeable event. Faced with the ‘hidden pedestrian crossing’ scenario, all subjects brake in the emergency situation in equivalent timeframes, but only experienced drivers combine braking with swerving toward the right before reaching the pedestrian. This probably reflects greater forecasting abilities and greater skill than beginner drivers (Berthelon, Mestre and Taramino, 1995; Underwood, Chapman, Bowden and Crundall, 2002). Faced with a ‘vehicle pulling out of parking’, DCTs take more time than EXPs to take their foot off the accelerator, this long response time, combined with high speed, led to collisions for three of them. Moreover, during the two first seconds of this scenario, DCTs drive more to the right of their traffic lane than EXPs; they then swerve to the left of their lane to avoid conflict with the other vehicle. Swerving to the right observed with the EXP group comes later and corresponds to calculated avoidance more than to an emergency manoeuvre. DCAs were not differentiated from the other two groups. As for the ‘sudden stop at a traffic light’ scenario, none of the variables analysed can be used to clearly differentiate driver behaviour as a function of their driving experience. This scenario causes an accident with an experienced driver whose high speed and the related low TIV did not enable him to react quickly enough to avoid collision. In the ‘vehicle overtaking and pulling back into the lane’ scenario, most drivers slow down sharply when the other vehicle merges in front of them, then they follow the vehicle until it turns right at the following intersection. Five drivers (three EXPs and two DCTs), however, adopt a strategy of overtaking the other vehicle. Singular behaviours attributable to EXPs mainly correspond to localised avoidances in all scenarios, but half of these avoidances were performed by a single driver with playful behaviour. For DCTs, singular behaviours notably correspond to collisions with the ‘vehicle pulling out of parking’. The absence of singular behaviours in the group of DCA drivers leaves us to suppose that they may adopt a more ‘safety-oriented’ driving style enabling them to manage situations better. It may be that the additional practice provided by this type of training enables them to acquire a certain number of skills. The small number of people in this group, however, allows us only to formulate this hypothesis and more research will be needed to confirm it. Lastly, the simulator used in this research does not have a gearbox, which may have masked inter-group differences. Our beginners in fact probably had more cognitive resources to allocate to the events occurring around them than if they had had to concentrate on changing gears, which we know can be hard to manage when people first start driving (Groeger and Clegg, 1997). The absence of a rear-view mirror should not have had any major effects on the behaviours observed. Consulting the information it provides was not an essential factor in the longitudinal and lateral checks made in the vehicle (except for changing lanes), which we know requires greater concentration among beginner drivers than experienced drivers (Lansdown, 2002).
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The results obtained in this study thus appear to be promising. Other scenarios need to be included in the driving simulators and, with a view to improving driver training, could be used to confront young drivers with ranges of difficult situations not commonly encountered in natural driving. These scenarios could then be used in the context of driver training insofar as they could increase awareness of the unusual situations which could potentially occur. Along these lines, Fisher et al. (2002) showed that inexperienced drivers trained to react to scenarios on a computer have less risky behaviour on the simulator than inexperienced drivers who have not had such training. Their behaviour, moreover, is closer to that of experienced drivers who have not had this training. Likewise, the work by Chapman, Underwood and Roberts (2002) suggests a transfer of skills learnt by video to driving behaviour.
References Berthelon, C., Mestre, D. and Taramino, R. (1995). ‘Anticipation visuelle de collisions en situations simulées: effets de l’expérience de la conduite automobile.’ Le Travail Humain, 58, 311–38. Blancher, G., Brenac, T. and Fleury, D. (1998). Scénarios types d’accidents liés à une interaction entre véhicules hors agglomération. INRETS/DRAST Convention Report No. 95MT0051/1. November 1998. Brenac, T., Delcamp, J., Pelat, S. and Teisseire, G. (1996), Scénarios types d’accidents de la circulation dans le département des Bouches-du-Rhône. INRETS Report, MA 9611-2, November 1996. Brenac, T., Nachtergaële, C. and Reigner, H. (2003), Scénarios types d’accidents impliquant des piétons et éléments pour leur prévention. INRETS Report No. 256, ISBN 2-85782-599-4, December 2003. Chapman, P., Underwood, G. and Roberts, K. (2002) ‘Visual search patterns in trained and untrained novice drivers.’ Transportation Research Part F 5, 157–67. Deery, H.A. (1999). ‘Hazard and risk perception among young novice drivers.’ Journal of Safety Research, 30, 225–36. Ferrandez, F., Brenac, T., Girard, Y., Lechner, D., Jourdan, J.L., Michel, J.E. and Nachtergaële, C. (1995). ‘L’étude détaillée d’accidents orientée vers la sécurité primaire : méthodologie de recueil et de pré-analyse.’ Presses de l’école nationale des Ponts et Chaussée, Paris. Fisher, D.A., Laurie, N.E., Glaser, R., Connerney, K., Pollatsek, A. and Duffy, S.A. (2002). ‘Use of a fixed-base driving simulator to evaluate the effects of experience and PC-based risk awareness training on drivers’ decisions.’ Human Factors, 44, 287–302. Fleury, D. and Brenac, T. (2001). ‘Accident prototypical scenarios: a tool for road safety research and diagnostic studies.’ Accident Analysis and Prevention, 33, 267–76. Gregersen, N.P. and Bjurulf, P. (1996). ‘Young novice drivers: towards a model of their accident involvement.’ Accident Analysis and Prevention, 28, 229–41. Groeger, J.A. and Clegg, B.A. (1997). ‘Automaticity and driving: time to change
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gear?’ In R. Rothengatter and E. Carbonell Vaya (eds), Traffic and Transport Psychology: Theory and Application , Oxford: Pergamon, 137–46. Hall, J. and West, R. (1996). ‘The role of formal instruction and informal practice in learning to drive.’ Ergonomics, 39, 693–706. Kemeny, A. and Panerai, F. (2003). ‘Evaluating perception in driving simulation experiments.’ Trends in Cognitive Sciences, 7, 31–7. Lansdown, T.C. (2002). ‘Individual differences during driver secondary task performance: verbal protocol and visual allocation findings.’ Accident Analysis and Prevention, 34, 655–62. Mac Leod, C. (1987). ‘Visual reaction time and high-speed ball games.’ Perception, 16, 49–59. Mayhew, D.R., Simpson, H.M. and Pak, A. (2003). ‘Changes in collision rates among novice drivers during the first months of driving.’ Accident Analysis and Prevention, 35, 683–91. Monarrez-Espino, J., Hasselberg, M. and Laflamme, L. (2006). ‘First year as a licensed driver: gender differences in crash experience.’ Safety Science, 44, 75– 85. Mourant, R.R. and Rockwell, T.H. (1972). ‘Strategies of visual search by novice and experienced drivers.’ Human Factors, 14, 325–35. Perrin, C., Van Elslande, P., Fleury, D., Girard, Y., Canu, B., Magnin, J., Parraud, C., Delage, B., Pascal, N. and Preget, A. (2004). Les études détaillées d’accidents: Renouvellement de la base. INRETS/DRAST Final Convention Report No. 03MT-32. November 2004. ISRN: INRETS/RE-04-914-FR. Ranney, T.A. (1994). ‘Models of driving behaviour: a review of their evaluation.’ Accident Analysis and Prevention, 26, 733–50. Salthouse, T.A. and Somberg, B.T. (1982). ‘Skilled performance effects of adult age and experience on elementary processes.’ Journal of Experimental Psychology: General, 111, 176–207. Sagberg, F. and Bjørnskau, T. (2006). ‘Hazard perception and driving experience among novice drivers.’ Accident Analysis and Prevention, 38, 407–14. Summala, H. (1987). ‘Young drivers’ accidents: risk taking or failure of skills.’ Alcohol, Drugs and Driving, 3, 79–91. Turner, C., McClure, R. and Pirozzo, S. (2004). ‘Injury and risk-taking behavior – a systematic review.’ Accident Analysis and Prevention, 36, 93–101. Underwood, G., Chapman, P., Bowden, K. and Crundall, D. (2002). ‘Visual search while driving: skill and awareness during inspection of the scene.’ Transportation Research F, 5, 87–97. Underwood, G., Chapman, P., Berger, Z. and Crundall, D. (2003). ‘Driving experience, attentional focusing and the recall of recently inspected events.’ Transportation Research F, 6, 289–304. Williams, A.F. (2003). ‘Teenage drivers: patterns of risk.’ Journal of Safety Research, 34, 5–15.
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Chapter 24
Investigating the Contexts in which In-Vehicle Navigation System Users Have Received and Followed Inaccurate Route Guidance Instructions Nick Forbes and Gary Burnett University of Nottingham, UK Introduction Automation has evolved within automobiles over the past few decades. Initially, subtle modifications supported the driver in braking (for example, ABS) and steering (for example, power steering) control tasks. More recently, a range of automated systems has been introduced to support (and even replace) higher-level tactical (for example, lane keeping, collision avoidance) and strategic (for example, navigation) tasks. Few authors have specifically considered in-vehicle navigation systems as automation, and most vehicle-automation research has focused on driver support systems that automate control tasks (for example, adaptive cruise control). Navigation system research has primarily focused on issues such as distraction, usability and interface design (see Burnett, 2000 for a review). However, in most automation taxonomies (for example, Parasuraman et al., 2000; Sheridan, 2002), navigation systems would be classified as a form of automated system replacing certain strategic tasks (for example, optimal route selection) whilst supporting tactical (for example, turn by turn route following) aspects of normal driving behaviour. Although current forecasts suggest that the global GPS marketplace (of which vehicle navigation is the largest sector) is yet to perform as well as expected (GPS market update, 2006), it is clear that millions of drivers have already adopted this technology (DfT, 2005; ITS, 2006). Surveys can provide a useful tool for understanding some of the issues faced by an established user population, especially from those who use these systems regularly and have integrated them into their daily lives. A literature review revealed two non-commercial (DfT, 2005; Svahn, 2004) and two commercial (Privilege, 2006, J.D. Power and Associates, 2003; 2004; 2005; 2006) surveys of navigation system users. The Department for Transport (2005) survey examined key user demographics, and the main focus of Svahn (2004) was navigation system usage in familiar and unfamiliar areas. The Privilege (2006) survey investigated distraction and system interaction while driving, and the J.D. Power and associates (2003–2006) surveys examined usage and user satisfaction. These surveys have revealed some interesting demographic trends. The DfT (2005) survey considered a wide selection of UK drivers’ and passengers’ attitudes
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towards transport. It revealed that an equal proportion of males and females reported using a navigation system (7 per cent) and although they were used by drivers of all ages, the highest using age bands were 26–44 years (9 per cent) and 45–54 years (9 per cent). Although the findings failed to attain statistical significance, data concerning job type and annual income also suggested a tendency for navigation system users to occupy higher socio-economic positions. Svahn (2004) surveyed German drivers who used the Volvo RTI (road and traffic information system) only. This is an integrated (that is, built into the car) navigation system. Just over three-quarters of participants were male (77.5 per cent), and their mean age was 45 years (45–49 years was the highest using age band). Most participants drove 30–35 000 kilometres per year (mean = 34 000 kilometres). Nearly three-quarters of participants (72.4 per cent) had a university degree or higher, suggesting that most navigation system users in this survey also occupied higher socio economic positions. Nearly two-thirds of navigation system users considered themselves expert computer users or as possessing considerable skills. Skilled computer users tended to use their systems more frequently, utilise greater functionality and worry less about safety considerations than those less skilled at computing. The Privilege (2006) survey aimed to compare the distraction potential of navigation systems and traditional maps while the vehicle is in motion. A higher proportion of navigation system users (19 per cent) reported losing concentration than those who used maps (17 per cent). One in ten drivers admitted to using system controls while driving and over half of these participants thought that in doing so, their eyes were taken off the road. Navigation system users took their eyes off the road for an average of ten seconds while travelling at an average speed of 60 mph. The survey also revealed that nearly one in eight drivers did not check a route they were unfamiliar with in advance and simply relied on their navigation systems to help them to reach their destinations. The J.D. Power and Associates surveys are carried out each year and are of particular interest to the automobile industry. In the survey for 2003, nearly half of all the participants who purchased a vehicle with a factory installed navigation system reported high levels of satisfaction, and many indicated that the presence of a navigation system could positively affect their vehicle purchase decisions. The J.D. Power and Associates (2004) survey identified ease of use (35 per cent) and routing capabilities (20 per cent) as the most popular components of user satisfaction. Just over three-quarters of participants in Svahn (2004) considered route guidance from their navigation system to be ‘of great value’ and 65 per cent thought there was ‘significant’ or ‘reasonable’ correspondence between system routing advice and their own individual preference when travelling in familiar areas. Although these findings indicate general user-satisfaction, clearly some navigation system users are less satisfied with their systems’ routing capabilities. In the J.D. Power and Associates (2006) usage and satisfaction survey, route or map related issues accounted for over 50 per cent of all problems cited by navigation system owners. The authors suggested dissatisfaction will diminish as the time between map production and delivery to customers decreases. However, in a continually evolving urban and rural transport system, it is unlikely that navigation system users will ever
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experience completely reliable route guidance. In some cases, drivers may simply arrive at incorrect destinations, but recently, anecdotal evidence has emerged in the news which suggests that drivers have received and consequently followed, a range of inaccurate and sometimes dangerous route guidance instructions. For example: Scenario 1 An 80-year-old German motorist has obediently followed his navigation system all the way into a huge pile of sand … ‘The driver was following the orders from his navigation system and even though there was a sufficient number of warnings and barricades, he continued his journey into the construction site,’ a police spokeswoman has said. (Source: http://www.news.com.au/story/0,23599,20555319-13762,00.html, accessed 24 May, 2007)
Scenario 2 [A] student’s car was wrecked by a train after she followed her sat nav system onto a railway track … she was trying to cross the line in the dark when she heard a train horn, realised she was on the track, and the train smashed into her car … ‘I put my complete trust in the sat nav and it led me right into the path of a speeding train’ she said … ‘the crossing wasn’t shown on the sat nav … I was using the sat nav completely dependent on it’. (Source: http://news.bbc.co.uk/1/hi/wales/south_west/6646331.stm, accessed 24 May, 2007)
Scenario 3 [S]atellite navigation systems have caused so many problems in one corner of the country that road signs have been put up to tell drivers they are heading for trouble. The bright yellow signs have gone up in the village of Exton, near Winchester in Hampshire, after lorries repeatedly got stuck in a narrow lane hardly wide enough for one car. (Source: http://www.metro.co.uk/news/article.html?in_article_id=37839andin_page_id=34, accessed 24 May, 2007)
The research reported in this paper aimed to investigate this phenomenon in light of these anecdotal accounts. Previous surveys have neither directly addressed perceived system reliability nor adequately highlighted instances in which inaccurate route guidance instructions were received or followed. Furthermore, surveys such as Svahn (2004) and J.D. Power and Associates (2003–2006) have only investigated drivers who used factory installed (integrated) navigation systems. A survey was designed which aimed to overcome the limitations of previous work by using a large, more diverse sample of navigation system users. A sub-section of a wider online navigation system user survey (Forbes and Burnett, 2006) was designed to examine perceived reliability and to highlight inaccurate route guidance instructions that participants have both received and followed. Participants were asked to report features they particularly liked and disliked about their navigation systems to determine the components of user satisfaction and dissatisfaction. Routing/map issues were expected to feature prominently in both lists. The survey also aimed to determine the frequency with which navigation system users updated
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their system maps and some of their thoughts about this. Although maps may never be completely reliable, there may be an association between the frequencies with which users update their maps and the extent to which they receive inaccurate route guidance instructions. It was envisaged that this study would form the foundation of future research to both explain the phenomenon, and suggest potential remediation. Method Design An online survey was developed with extensive piloting ensuring that all questions and options had good clarity with a clear structure. A subset of 27 variables was considered relevant to the present paper. Age, number of years driving, mileage, months using navigation system and months using current map were ratio level variables. Computing skill, map update frequency and map update cost were ordinal level variables. Gender, employment status, navigation system type, navigation system experience, map update, reasons for map update/non-update, future map update, map update importance, route guidance reliability/efficiency, and dangerous/ illegal route guidance instructions received and followed were nominal variables. Nationality, system features particularly liked or disliked and the ‘other, please specify’ responses for variables in italics required text entry. Participants Eight hundred and seventy-two participants (844 male, 28 female, mean age = 46 years, range = 17–79 years) provided data for the survey. All participants were self-selecting, having responded to an online advertisement placed on driving and navigation system related Internet forums, bulletin boards and mailing lists. Apparatus Participants used their own computers to complete the questionnaire from remote locations. The questionnaire was designed using Microsoft Excel and html authoring software. Procedure Participants responded to online advertisements and were directed to follow a link to the questionnaire, provided that they had held a full driving licence for at least six months and used an in-vehicle navigation system. They were asked to answer questions truthfully and were reminded that their responses were completely anonymous.
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Results Participant demographics Figure 24.1 shows all age bands were well represented, but female participants were severely under-represented in the sample. Most participants were from the UK (59 per cent), USA/Canada (26 per cent) or the rest of Europe (10 per cent). The majority (96 per cent) were employed or retired, and most considered themselves as expert computer users (35 per cent) or possessing considerable skills (43 per cent).
120
Number of participants
100
80 Female
60
Male
40
20
0 25). For each of the personality dimension extracted from the EPQR-test, as well as for the cognitive failure scores, participants were time separated in two groups based on the median value of the entire group. A transformation was made to participants’ responses to ensure normality of distribution according to assumptions of the ANOVA method. Inverse of square roots was used for mode and mean of RT, and square root was used for percentage of error, misses and lapses. Results Variance of reaction time Separate ANOVAs indicated that the RT distribution was significantly affected by age group [F(1,12) = 5.81, p < 0.05], gender [F(1,12) = 5.35, p < 0.05], extraversion score [F(1,12) = 4.4, p < 0.1], and Body Mass Index (BMI) [F(1,12) = 3.22.4, p < 0.1] (Table 29.2). Results show that younger participants, females, participants with low extraversion score and participants with BMI lower than 25, have a shorter RT than, respectively, older participants, males, participants with high extraversion score, and participants with BMI higher than 25 (Figure 29.2). No other significant effects were found for the other subjective factors. Table 29.2 Impact of extraversion on performance metrics
Metric Mode RTa Mean RTa Variance Lapses %b
Age F (1,12) p 5.811 0.033* 9.105 0.011* 5.887 0.032* 5.217 0.041*
Genre F (1,12) p 5.355 0.039* 4.417 0.057 5.205 0.042 4.937 0.046*
a
ANOVA conducted on transformed data ( 1
b
ANOVA conducted on transformed data ( x ). p < 0.05
*
x ).
Extraversion F (1,12) p 4.406 0.057 5.808 0.033* 5.402 0.038 7.539 0.017*
BMI F (1,12) p 3.224 0.098 3.953 0.070 4.978 0.045* 9.869 (1,12)=5.22, p D, not C ≈ D, therefore reducing the likelihood of a rider finding himself in a C < D situation when task demand changes. When a rider who is in a flow state is operating on automatic, the skills being used must also be automatic and have been learnt by repeated practice. If any of these skills are inappropriate they should be replaced by correct skills, and the new skills will have to be well practiced too. Training that intends to alter the skill set of a rider, rather than just build upon existing skills, needs to take into account the automaticity that accompanies flow riding (Broughton, 2006b). Ideally for interventions to be most productive they must be targeted at the correct audience, for example skills training for those who are more inclined to ‘rush based enjoyment’, and behavioural training for riders inclined to ‘challenge based enjoyment’. With enjoyment profiles for riders being complex, and liable to be different for individual riders, profiling prior to any non mass media interventions could be beneficial. Profiling can allow interventions to be targeted to the needs and goals of the individual rider. The enjoyment profile differences between riders also need to be considered when mass media interventions are being designed as one size does not fit all! Conclusion There is evidence that riders may be seeking a flow type experience by matching their riding skills to the challenge presented by the riding environment. This is equivalent to task demand being closely matched to capability (Fuller, 2005), which can be expressed as C ≈ D. When a rider is in this state then there is a very small safety margin. If task demand rises, or capability drops, (C < D) then a rider would be out of control which may result in a collision. Skills training may increase rider capability; therefore a higher task demand would be required to achieve a C ≈ D state. As speed is a major enhancer of task demand then it would be logical that skills training may entice riders who are seeking a flow experience to ride faster. Because of this, any skills based intervention should incorporate methods to help prevent this from happening (Mannering and Grodsky, 1995; Ormston, Dudleston, Pearson and Stradling, 2003). As enjoyment is a major riding goal, then any safety intervention must respect this. If an intervention does not acknowledge, or attempts to remove, this goal, then it is likely that riders will reject the intervention and it will be ineffective (Broughton, 2006a). Conversely, as the majority of riders do not ride because of the risk, most would accept interventions that reduced the risk provided there was not an erosion of enjoyment. Interventions should also be targeted at specific rider enjoyment types, therefore rider profiling techniques need to be developed. By targeting interventions, and preserving rider goals, interventions can be developed that will aid in reducing PTW KSI and help to meet Government road safety targets.
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References Aarts, L. and Van Shagen, I. (2006). ‘Driving speed and the risk of road crashes: a review.’ Accident Analysis and Prevention, 38, 215–24. ACEM (2000). Smart Wheels for City Streets. Brussels: ACEM. ACEM (2004). Maids: In-depth Investigations of Accidents Involving Powered Two Wheelers. Brussels: ACEM. Broughton, P.S. (2005). ‘Designing PTW training to match rider goals.’ In Dorn, L. (ed.), Driver Behaviour and Training. Aldershot: Ashgate Publishing. Broughton, P.S. (2006a). ‘The Implication of the Flow State for PTW Training.’ In Behavioural Research in Road Safety 2006, Sixteenth Seminar. London: DfT. Broughton, P.S. (2006b, 27th Feb–1st March 2006). ‘Influencing Power Two Wheeler Behaviour.’ Paper presented at the 71st RoSPA Road Safety Congress: The Road to Safer Behaviour, Blackpool Hilton Hotel, Blackpool. Broughton, P.S. (in press). Risk and Enjoyment in Powered Two Wheeler Use. Edinburgh: Transport Research Institute, Napier University. Broughton, P.S. and Stradling, S.G. (2005). ‘Why ride powered two wheelers?’ In Behavioural Research in Road Safety 2005, Fifteenth Seminar. London: DfT. Clarke, D.D., Ward, P., Bartle, C. and Truman, W. (2004). In-depth Study of Motorcycle Accidents (No. 54). London: DfT. Csikszentmihalyi, M. and Csikszentmihalyi, I.S. (1988). Optimal Experience: Psychological Studies of Flow in Consciousness. Cambridge: Cambridge University Press. Csikszentmihalyi, M. (1990). Flow: the Psychology of Optimal Experience. New York: Harper and Row. Csikszentmihalyi, M. (2000). Beyond Boredom and Anxiety: Experiencing Flow in Work and Play. San Francisco: Jossey-Bass. Cunliffe, N. and Stradling, SG. (2006, 27th Feb–1st March 2006). Riders Improving and Developing Their Experience: RIDE Programme. Paper presented at the 71st RoSPA Road Safety Congress: The Road to Safer Behaviour, Blackpool Hilton Hotel, Blackpool. DfT (2000). Tomorrow’s Roads: Safer for Everyone. London: DfT. DfT (2004). Tomorrow’s Roads: Safer for Everyone – The First Three Year Review. London: DfT. DfT (2005). The Government’s Motorcycling Strategy. London: DfT. DfT (2006). Compendium of Motorcycling Statistics 2006. London: DfT. DfT (2007). Second Review of the Government’s Road Safety Strategy. London: DfT. Dietrich, A. (2004). ‘Neurocognitive mechanisms underlying the experience of flow.’ Consciousness and Cognition, 13(4), 746–61. Fuller, R. (2005). ‘Towards a general theory of driver behaviour.’ Accident Analysis and Prevention, 37(3), 461–72. Goldenbeld, C., Twisk, D. and de Craen, S. (2004). ‘Short and long term effects of moped rider training: a field experiment.’ Transportation Research Part F: Traffic Psychology and Behaviour, 7, 1–16. Kipling, J. (2005). Inside the Mind of a Motorcyclist to Reduce Accidents. Retrieved
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14 April, 2006, from http://www.devon.gov.uk/index/your_council/inside/ directorates_and_departments/chief_executive_s/communication/news_service/ press-releases/press_psychology.htm. Mannering, F.L. and Grodsky, L.L. (1995). ‘Statistical analysis of motorcyclists’ perceived accident risk.’ Accident Analysis and Prevention, 27(1), 21–31. Ormston, R., Dudleston, A., Pearson, S. and Stradling, S.G. (2003). Evaluation of Bikesafe Scotland (No. 169/2003): Scottish Executive. RoSPA. (2006). Motorcycling Safety Policy Paper. The Royal Society for the Prevention of Accidents. Rutter, D.R. and Quine, L. (1996). ‘Age and experience in motorcycling safety.’ Accident Analysis and Prevention, 28(1), 15–21. Sexton, B., Fletcher, J. and Hamilton, K. (2004). Motorcycle Accidents and Casualties in Scotland 1992–2002. Edinburgh: Scottish Executive. Sexton, B., Hamilton, K., Baughan, C., Stradling, S.G. and Broughton, P.S. (2006). Risk and Motorcyclists in Scotland. Edinburgh: Scottish Executive. Wickens, C.D. and Hollands, J.G. (2000). Engineering Psychology and Human Performance (Third ed.). New Jersey: Prentice Hall.
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Chapter 34
Understanding Inappropriate High Speed by Motorcyclists: A Qualitative Analysis Barbara Hannigan,1 RayFuller,1 H. Bates,1 Martin Gormley,1 Steve Stradling,2 Paul Broughton,2 Neale Kinnear2 and C. O’Dolan2 1 Trinity College Dublin, Ireland 2 Napier University, UK Introduction Higher speeds are associated with increases in the probability of crashing and the severity of the outcome (Aarts and van Schagen, 2006; Federal Highway Administration, 1998). In the context of informing developments in road safety, these relationships make the understanding of the conditions for inappropriate high speed a particularly compelling issue. Proportionately, more motorcyclists exceed the speed limit than car drivers (Fuller et al., 2007). However, excessive speed does not appear to be a major cause of motorcycle accidents. What seems to be much more relevant is inappropriate high speed for conditions leading to loss of control, such as on a bend or in the event of encountering something hazardous and unexpected like an oil spill or debris on the road (Clarke et al., 2004). Just as with car drivers, lack of experience can reduce capability, and motivation for speed can increase task demand, increasing the difficulty of the task and narrowing the rider’s safety margin (Elliott et al., 2003; Baughan et al., 2004). There is also some evidence of an adolescent riding subculture that reinforces high risk behaviour (Bina et al., 2006). In order to further our understanding of inappropriate high speed (IHS), a threepronged strategy was adopted involving a review of the available English language literature published in the 12 years 1995 to 2006, a focus groups study and a national (Great Britain) survey of drivers. Preliminary results of the literature survey may be found in Fuller et al. (2006). The results of the national survey are reported in part in Stradling et al. (2007). This report is specifically concerned with the results from one group of the focus groups study: the riders of powered two wheelers (PTWs), who will also be referred to hereafter as ‘bikers’ or ‘riders’, as these are the terms the participants use. Before describing that study, a summary of theoretical developments in the Task-Difficulty Homeostasis model that were informed by the literature review (Fuller et al., 2007) will be presented. This model provides a theoretical framework for understanding speed choice and will be evaluated as a basis for the thematic organisation of the results from the focus group.
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The Task-Difficulty Homeostasis model In the context of speed choice, task difficulty homeostasis basically refers to the process where bikers adjust their speed so that the perceived difficulty of the bikeriding task falls within a range of difficulty that the biker is prepared to accept. Perceived task difficulty arises out of the interacting interface between perceived task demand and perceived capability (see Figure 34.1 below), hence the original name of Task-Capability Interface model (Fuller and Santos, 2002). Speed directly affects task demand (Fuller et al., 2007). Research also shows that in the bike-riding task, difficulty and feelings of risk co-vary, enabling us to refer to the upper level of difficulty a biker is prepared to accept as the risk threshold (see Figure 34.1).
range of acceptable task difficulty and risk threshold
comparator
decision and response perceived task difficulty/risk perceived capability
effects on vehicle speed
perceived task demand
Figure 34.1 Representation of the basic process of Task-Difficulty Homeostasis From distal to proximal determining factors, perceived capability arises out of the biker’s basic physiological competence, from which starting point the combined effects of education, training and experience produce the individual’s competence as a rider. However this competence may be undermined to varying extents by human factor variables such as drowsiness, stress and emotional state, resulting in the rider’s actual available capability and the biker’s perception of this (see pathway to ‘perceived capability’ in Figure 34.2). Perceived task demand arises out of the handling and other operating characteristics of the bike, driving route, time of day, the physical road environment (in particular visibility level, road alignment and road surface characteristics) and the presence
Understanding Inappropriate High Speed by Motorcyclists disposition to adopt particular range of task difficulty and risk threshold
disposition to comply with speed limit
immediate influences on risk threshold effort motivation
immediate effort
goals of journey
immediate driving goals
range of acceptable task difficulty and risk threshold
immediate influences on compliance comparator
decision and response perceived task difficulty perceived capability
human factor variables
education training experience
physiological competence
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effects on vehicle speed
perceived task demand
road environment behaviour of other road users
route time-of-day
vehicle characteristics
Figure 34.2 Representation of the process of Task-Difficulty Homeostasis, distinguishing between proximal (clear boxes) and distal (grey boxes) determinants and influences on compliance and behaviour of other road users. It also critically involves the speed at which the biker is travelling (see pathways to ‘perceived task demand’ in Figure 34.2). The biker’s range of acceptable task difficulty (and its upper level defined as the risk threshold) is partly determined by her or his perception of current capability. It is also determined in part by the distal and more immediate goals of the journey, the degree of effort the biker is prepared to invest in the task and the biker’s disposition to adopt a particular range of task difficulty and its related risk threshold. Beyond this dispositional characteristic, there may also be more immediate influences on risk threshold, such as an acute feeling of anger (see pathways to ‘range of acceptable task difficulty and risk threshold’ in Figure 34.2). Finally, the result of the influence of all of these components may determine a choice of speed that actually exceeds the posted speed limit. Thus we need to include some representation of the biker’s disposition to comply with the speed limit and also more immediate influences on compliance (see pathways to ‘decision and response’ in Figure 34.2).
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Specific aims of the study Questions arise, however, as to the extent to which this theoretical formulation coincides with bikers’ own perceptions and experiences regarding speed choice and how this compares with that of other road users. In order to capture a range of roaduser perspectives, four separate focus groups were established. One consisted of professional drivers, two consisted of participants who had been convicted for a speeding offence and who had opted for a speed awareness course as an alternative to receiving penalty points and one consisted of riders of powered two-wheeled vehicles. This report focuses mainly on the perspectives of the bikers. The use of focus groups appears to provide an appropriate method to explore this issue, providing at the same time an evaluation of both the model’s face validity and its content validity. Thus the principal aim of the study was to elicit rider experiences and perceptions regarding speed choice and speeding behaviour. A second aim was to determine the extent to which the themes identified by participants could be explained using the concepts of the Task-Difficulty Homeostasis model. A third aim was to examine the implications of the results for the content of speed-related media safety campaigns. Method Qualitative research methodology Qualitative research comprises a diversity of approaches to enquiry in the health and social sciences that address the meaning of verbal text. It is more subjective than quantitative research; more exploratory than confirmatory; more descriptive than quantitative; more interpretative than positivist (see Denzin and Lincoln, 1994). It is predicated on the assumption that subjective experience is at least as important as ‘objective’ reality. Interpretative research nonetheless requires a trail of evidence throughout the research process to demonstrate credibility, transparency and trustworthiness (Koch, 1994; Aroni et al., 1999). The guiding theoretical framework underpinning the methodology was a humanistic-phenomenological one. The process of analysis described shows how thematic findings were derived from the participants’ original transcripts. Thematic analysis Thematic analysis is a search for themes that emerge as being important to the description of the phenomenon under study (Daly, Kelleher, and Gliksman, 1997). The process involves the identification of themes through careful reading and rereading of the data (Rice and Ezzy, 1999). It is a form of pattern recognition within the data, where emerging themes become the categories for analysis.
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Participants The participants whose voices are represented in this report were nine motorcycle riders, ranging in age from 18 to 50 years, who participated in a focus group, held on February, 22nd 2006, in Edinburgh. They were from a convenience sample selected to provide homogeneity (all adult bikers) as recommended by Morgan (1997), and partly to ensure a diversity in range of ages and type of bike-riding experience. The powered two-wheelers focus group was included in the wider context of the study on IHS in particular for their potentially unique perspective on inappropriate high speed. No attempt was made to obtain a sample that might be representative of the UK biker profile. The participants were sourced through professional contacts or groups of people accessible to the research team and were invited to attend as volunteers. They were paid £50stg for their participation. None of the participants were known to the group facilitator or identifiable to the researchers during the process of data analysis, except through a designated identification number. Their views provide us with a snapshot of personal opinions and perspectives detailing accounts of their own and others’ experiences and narratives about issues related to speeding. Procedure All discussion from the focus group session was recorded on audio-tape to facilitate subsequent transcription. The session lasted approximately one hour and 45 minutes. Issues for the group discussion were loosely guided by a broad set of themes which were agreed earlier by the research team (see Table 34.1). At the beginning of the session, the facilitator introduced the topic in general and requested that the participants exchange their views and opinions. Agreement or consensus was not a goal of the discussion. The group was encouraged to expand on topics through the use of open questions and validating prompts by the facilitator such as ‘that sounds interesting, can you tell me more about it?’ or ‘does anyone have an alternative view to this?’. The main themes that emerged during the discussions were reflected back to the group to check that they had been fully and accurately heard and to provide an opportunity to the participants to either clarify or expand on their views of the topics raised. Method of analysis Transcripts were initially generated from each audio recording and meaning units identified by four researchers. Emergent categories were then cross-checked. In the next stage, thematic clusters were identified and subsequently organised in terms of the concepts of the Task-Difficulty Homeostasis model. These two steps were carried out by two researchers independently and the results compared. Any discrepancies were resolved through discussion. Thus the data that emerged from the
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Table 34.1 Broad themes discussed during the focus group Interview Themes
Aspects Considered
Speeding adjectives Attitudes to speeding Factors influencing speed Internal and external Speed reduction influences Factors relating to speed limit compliance Control and loss of control Road traffic accidents/collisions Speed choice Speed and legal issues
Completely open Positive and negative
Bike/car issues/ Causes of speeding
Effective and ineffective Assist/ reduce compliance Internal/external factors Personal experiences/impact Different contexts Penalties/issue of being caught/ appropriateness of limits Similarities and differences Mood and emotion
Causes of slowing Speeding stereotypes Consequences of speeding Spontaneous recommendations from group General driving behaviours influencing speed
Internal and external Gender Legal/ personal / Risk/safety issues Other types of dangerous driving
focus group facilitated the themes to emerge from the data using inductive coding of the phenomena. The step by step process is outlined in Figure 34.3. Results In the results, statements made by individual participants are identified by a number allocated to each participant during the group process to ensure confidentiality. The views expressed should not therefore be taken as necessarily representative of all group members unless consensual agreement is indicated. Nevertheless, there was frequent open agreement and little dissent occurred. Italicised words in parenthesis are added for clarification. Emergent themes are organised in relation to key concepts of the Task-Difficulty Homeostasis model. Task difficulty homeostasis Bikers made some reference to using speed as a means of maintaining a desired level of difficulty or safety margin: Well, I, could control the safety margins with the speed. I feel quite happy doing 80–85, but if something, if the weather.., if conditions got worse, if the rain gets heavier, then I would slow down, I would kinda back off. (PTW 2)
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Report to research team
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Finally report findings Research team Research concept
Collate audio tape, text and transcribe
Identify units; essence of what was said
Develop categories and verify
Interpretatively determine connections & create clusters
Verify and cross check
Figure 34.3 Schematic illustration of the research process Some bikers referred to this desired state as a ‘comfort zone’: And again it was on the motorway, nobody else about, did it (high speed) for a couple of minutes, stopped whenever there was anything looking like it was getting too close. Just a bit too much sensory input for me, and a little bit too quick, even though feels like an empty road, it doesn’t feel comfy. (PTW 8)
The desired state may be informed by the biker’s sense of control: The fastest I’ve ever been on a bike is 130 and that is 15 miles an hour short of what the current bike can do, but I shut it down for various reasons: one, I suddenly realise what the hell happens if someone catches me; but two, it was starting to get busy in front of me as well, traffic was coming towards me at a hell of a fast rate, and I just wasn’t prepared to take a chance. Umm, if somebody gave me carte blanche and a fairly empty bit of motorway, sure, I would wind it on and see: a) what the bike could do; and b) what it felt like. But, to be frank, I don’t find it that enjoyable, because I wasn’t overcoming any
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Human factor variables that may influence capability Bikers mentioned the importance of loss of concentration as contributing to a collision: The only time I had a crash on the bike, in sort of twenty nine years of riding, was I had taken the bike out, err, I had been setting up the suspension, after being down to the bike show, late in the afternoon, slowed down to 60, having been going a little bit faster, testing things out, and I just lost concentration and went into a corner, woke up about ten, twelve yards from the corner and went shit, going far too fast, hit the brakes, drifted wide, and ended up in a ditch. Luckily I walked away from it with just cuts and bruises. OK, that was because my concentration had switched off because there was not enough to think about, having been going quickly for two or three hours. (PTW 1)
They also mentioned that concentration may suffer at low speeds and that recovery from fatigue after a break enables riding at higher speeds. Factors that may influence task demand All groups referred to road design and other road characteristics and to traffic conditions as influencing speed choice: I always ride at a speed appropriate for conditions, and it will not necessarily be the speed posted on the signpost, there is so much disparity between 30 limits, 40 limits. Well 30 limits could well be 40 limits and on some roads 30 is fine, it is not a problem. On other roads it depends on the conditions at the time, and they’re not always necessarily appropriate. I think they should be more variable… (PTW 1)
…Depends on the road conditions, and where you are on the road – schools and the likes of that, where there’s possibility of lots of pedestrians, or compared to urban motorways/ dual carriageway, more likely to speed on the open road… (PTW 4)
Related to this, bikers indicated that familiarity with the road was also a factor: There is also a factor as well with roads that you are familiar with; you are probably going to drive, or ride, quicker on them than on roads that you don’t know. It’s just because you have a reasonable expectation of driving down this road, if you do it this sort of speed most of the time you will have no problems. You try doing it on, probably an identical piece of road that you are not familiar with and its like, ‘what’s round this corner’, ‘what’s happening here’ – so there is an element of that. (PTW 1)
Bikers also referred to the characteristics of their bike:
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If you are in a car and you want to accelerate, you got to be in the right gear and it takes time, on a bike its just, boom, and you’re there. You set the speed with your wrists. Not without, with hardly any time delay, brakes equally well. It’s highly effective and almost instantaneous. If you want to be there to there, you can do it in almost instantly, and it’s often said in the bike press, you think where you want to be and the bike goes there. And they often say if the bike handles really well, you just have to think it round the corner. You constantly read that in the press, and that is part of it. There is no, there is very little constraints on what you want to do from the vehicle. (PTW 6)
They also noted the influence of weather conditions: If it’s in the summer, I may well ride faster than I would in the winter. In the winter time, certainly when its getting round about the freezing, I will be concerned about the possibility of ice and slowing down. (PTW 1)
and the potentially dangerous behaviour of other drivers: I wouldn’t be watching the van because there’s a vehicle, a couple of vehicles in front of the van and one of those might do something, might make the van swerve into your lane and that’s what I would be, I would probably, in fact, possibly slow down until I got past the first car. And I could eyeball that group of vehicles up there before shooting past them. (PTW 6)
Distal and proximal factors which affect range of acceptable task difficulty and risk threshold At a distal level, one participant stressed the importance of experience in moderating his range of acceptable task difficulty and risk threshold: But the one time that I was on a fairly clear stretch of road, it was a motorway with not much traffic, and I thought I would just see if I could get it up to a hundred, just to see, because I never did it before and I thought that I would give it a go, that’s what I thought, the roads quite clear, I know this road, it’s fine, and I just about to get to one hundred, ninety-five, and a hawk flew out from the side of the road, and I slowed down and I just wing clipped the front of my bike as it went, and I thought well, is that someone trying to tell me that I should not be driving this fast? But I have not tried again since then because my thinking was, well, there was no way to predict that ever happening at that exact moment. (PTW 9)
At a proximal level, the participants experienced high speed as a great feeling: Yeah, it is, it’s a great feeling. Your head feels empty, you’re just scooting along and you’re going ‘this is the business’. You know a bit of speed and the first time you do it, Wohoh, look at me! You know… (PTW 7) That is the biggest thing that I have noticed. Cause it is, you’ve got that exhilaration feeling, you’ve got wind rushing past you. It’s great, isn’t it? You don’t experience that in a car. (PTW 7)
They described the equivalent of their risk threshold:
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and the desire to experience a feeling of power: …And there is a certain amount feeling of, I don’t know, bravado, perhaps, and a certain macho type of thing… There’s a sort of thing about being on the bike, rather than in a car, and you feel that you are a bit more at one with the world. (PTW 6)
Other influences which tended to raise risk threshold for all participants were to see how fast the vehicle could go: Every bike I have had I’ve taken it to the limit at some point. (PTW 2) the expression of frustration or annoyance: Frustrations and that, getting held up by cars, trucks and buses, want to get past them to a bit of clear road and will do the speed needed to get past them. (PTW 4)
pressure from other drivers/riders: You normally get, it normally works out that the person who is going the fastest ends up at the front and everyone else is playing catch-up, and it takes a certain amount of self discipline to say ‘No, I don’t want to go that fast’, and a lot of young, younger people become croppers without the experience, they get into a group of people and think that they have got to keep up, and nobody said to them ‘Look, we’ll wait for you’. (PTW 6)
competitiveness and a desire to impress: I would say that a lot of the, a lot of the fun for me, is trying to get by on old, or substandard, or, you know, antique transport. You get a lot of satisfaction from, you know, from keeping up with modern traffic on something 30 years old, ok it’s … leaking again, ok, … but, I’ve a friend who is constantly rebuilding a three-cylinder Triumph cause he tries to drive it like a sports bike, cause all his mates have got sports bikes. But he keeps up with them, only for three months of the year, then its in pieces again. (PTW 8)
…You know the kind into speeding, at the top that’s going to be the kind of weekend warrior types with the one-piece leathers and the state-of-the art sports bike. And the pressure is on them cause they’ve got a point to prove. (PTW 5)
Bikers pointed out, however, that their behaviour often depends on the prevailing mood they happen to be in:
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But then again to be honest, it depends how I felt, if I was in the mood, I would maybe take it up a bit faster, then again depending how I feel I might go a bit slower, you know, just how I felt on the day. (PTW 3)
Factors affecting journey goals There was agreement about the pressure of being late: Part of the business is if you are trying to get to somewhere for a certain specific time, and you have been held up or whatever, its like yeh, I am going to try and get there, ummm, not always a sensible decision, but it happens, so you may have the pressures of making an appointment, or whatever, and I’ll, you know, drive faster than I probably really want to. (PTW 1)
And keeping with the traffic flow is a common immediate influence on driving goals: …but certainly at the bottom end of the M1 or something, in a car everybody, and someone was saying just now, is doing 85, 90 miles an hour, in a, like a train, and if you do less then you are getting in everybody’s way, and it is, in my mind, becoming more dangerous. I wouldn’t actually want to be on a bike in that sort of a train, because I would feel that if anything went wrong, I am meat in a sandwich. (PTW 6)
For bikers a further goal may be to test one’s skill: There are occasions when… when you go out… to, sort of, test yourself, almost, and you sort of say I am just going to go a wee bit faster around that bend. Biking is all about going around bends. (PTW 6)
Factors influencing compliance with speed limits Dispositional influences Bikers generally agreed they should comply with speed limits and that non-compliance could lead to a dangerous loss of control: Awareness of what can go wrong, the penalties of getting it wrong, you are much more aware of what is going on around you, because on a bike if you miss a potential hazard it can kill you, if you miss it in the car it will dent the car and maybe knock you out, there is a significant difference in the penalty for getting it wrong on a bike, and you tend to be that much more aware. (PTW 6)
Enforcement is commonly regarded as encouraging levels of compliance: I am aware that if I was caught speeding then it involves penalty points and I drive for a living so that keeps me on a much more sort of even keel, you know… (PTW 7)
Bikers also indicated that compliance may be induced by feelings of shame. Hence speed violations are more likely to occur when there is no-one else looking:
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Driver Behaviour and Training – Volume III …on the open road I will do what I think is acceptable, I am always concerned about the image, impression I give to other people about the rate at which I go, so I am more likely to speed when there’s nobody around. (PTW 6)
Training and age were reported to have influenced levels of compliance with speed limits: When I started riding, about 20 years ago, I automatically thought that 40 meant 50 and 30 meant 40 and 70 meant as fast as you could go on a small and knackered old bike, and now I am pretty strict in town and… I think that comes with doing an advanced driving course. (PTW 8) I concur with that a lot, I always thought of speed limits being ugly – 10 miles over – it was my standard. I now don’t exceed 30 in a 30, I normally try to stay down at 20 in 20s while going down my high street for example, and totally ignore the people behind me who want to go faster… I have slowed down and accepted the speed limits as being what I should be doing, probably only in the last 5 years possible even really, and part of that is the result of doing an Institute of Advanced Motorists (IAM) training and showing a commitment to that, and just getting older. (PTW 6)
All groups also agree that they may not comply when the speed limit is perceived to be too low for the road characteristics – the speed limit needs to make sense: …when it was busy, forty limit, past road-works… even when maybe there weren’t many people working there, most people were respecting the speed limit. Seven o’clock at night, quiet, no not much traffic, nobody around, everybody was thinking, ‘what the hell are we doing forty for?’, and they are all doing fifty, and it was perfectly safe, umm. (PTW 6)
They also indicate that it can be difficult to recalibrate one’s speed when entering a lower speed zone and that the distraction of frequent checking of the speedometer can be dangerous: I would be concentrating on the road. As it’s been said there is no point in taking your eyes off what could happen because somebody could pull out in front of you or swerve from the hard shoulder into the central reservation and you want that extra tenth of a second. You don’t care about the speedometer, it’s nonsense. What you are caring about is, you’re caring about getting there safely and avoiding any potential hazards. (PTW 1)
Group comparison Bikers agreed with car drivers in the other focus groups that speeding is easy and can be unintentional. Influences which raise speed include pressure from others, keeping with the traffic flow, seeking an adrenaline rush and in response to frustration or annoyance. Vehicle size or type and seeing how fast the vehicle will go can also induce increased speed. However, unlike the bikers, drivers reported that speeding is common – everyone is in a hurry. Bikers see most riders as sticking to the speed limit. Like the professional drivers, they refer to riding in a ‘comfort zone’, but also talk about ‘getting into a groove’ and being ‘at one’ with the bike. Speeds which induce this state may be over the limit:
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…you get into, emm, a sort of close relationship with the bike and what you are doing. And everything becomes sort of semi-automatic and it’s going well and that can make you forget about the speed limit because you are just enjoying it so much and the experience that you just ride the bike and you suddenly wake up, oh, what was that, I’m going fast. And you’re going fast against the speed limit, not against what’s possible. All right, but, the, ehh, ehh, what keeps you doing that is its just bloody enjoyable. You know you get a great deal of pleasure out of doing that. And you get a sense of satisfaction and achievement as well. Emm, which you don’t normally get in a car. Because it is not so controlled, not so adaptable and not so flexible, emm, as, as a bike is. (PTW 6) It’s like the sweet spot on a golf club or a tennis racket (PTW1)
Like the drivers on the speed awareness course, bikers think that exceeding the speed limit is not necessarily unsafe. They are more likely to comply when the limit makes sense. They see as general influences on speed ‘common sense’, the time available to respond, weather conditions and fear of mechanical failure at high speeds. Avoidance of feelings of guilt and shame similarly helps motivate compliance. Also in similar vein to those drivers, bikers may be influenced to go faster by others and the desire to impress with a demonstration of skill. Speeding can be fun but is also mood dependent. Bikers say that low speeds can impair concentration. They recognise that the safety margin is up to the rider and leave space to enable avoidance actions if necessary, try to keep braking to a minimum and slow down to deal with the dangerous behaviour of other road users. They are aware that bikes are less stable than other vehicles, are not so easily seen and that riders are particularly vulnerable. Discussion From the main content of participants’ comments it may be seen that the themes can be readily mapped onto the conceptual framework provided by the Task-Difficulty Homeostasis model and no theme emerged that could not be accommodated by the model. For this reason the results have been presented using the model’s framework. Influences on speed choice are easily translated into the guiding theoretical concepts of driver capability, driving task demand, driver task difficulty, risk threshold and disposition to comply with speed limits. In fact the only dimension of the model not specifically identified by participants’ comments was that of effort motivation. Bikers reported using their speed in order to control safety margins and to obtain a ‘comfortable’ state. Rider capability was regarded as being affected by experience, training, stress, age, concentration and fatigue. The demands of the task of riding a powered-two-wheeler were seen to be influenced by road and traffic conditions, including familiarity with the road, bike characteristics, weather conditions and the behaviour of other road users. There was awareness that high speeds reduced the time available to deal with contingencies and riders wanted to keep braking to a minimum, particularly sudden hard breaking, to avoid loss of control. On the other hand there was general agreement that speed could be pleasurable and could deliver an adrenaline rush and a feeling of power. Speed increases could also be triggered
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by pressure from others and by competitiveness. Sometimes bikers used speed to test their skill and the capacity of their bike, particularly when it was a new machine. Others felt that the need for this changed with age and maturity. Non-compliance with the speed limit was generally regarded as potentially dangerous, but not necessarily so where road and traffic conditions permitted. The margin above the limit, allegedly ‘permitted’ by enforcing agencies, is regarded as authorising minor levels of infringement and there is no shame felt in engaging in this behaviour. Going over the limit can also happen unintentionally, especially where signs are not detected: compliance is much easier when the limit makes sense. There was a greater tendency to speed when there was a perceived reduction of risk such as on non-urban roads with low traffic or when there was a lack of potential witnesses. Thus the conclusion has to be that the evidence arising from the focus group content is adequately captured by the model and is within its range of convenience. Riders’ own perceptions and experiences regarding speed choice are entirely consistent with the theoretical formulation and provide for a rich elaboration of instances of its key concepts. The focus group method in addition enabled capture of the narration of dynamic episodes, in contrast to the unitary responses typical of questionnaire surveys. For example, consider this narrative about ‘putting down’ other motorcyclists: I can think of two or three friends, I’m not actually a good enough driver to do this very much I don’t think, but I certainly got two friends who got old, old and British bikes and trigger for them is someone with something newer, faster, bigger, more modern, and they go ‘ah, look at that’, and especially if it is someone who has fairly obviously recently just passed their direct test, and got a big sporty bike and you can just see that they are a little bit wobbly on the corners. It’s let’s just show them what they have wasted seven or eight thousand pounds on. And its not clinical speeding, this will tend to be, single track roads… ummm and you can just see them (the two friends) disappear into the distance, and you see them fifteen miles away with a big smirk on their face, and I’ll kinda try and maybe stay with these bikes but, but they will be determined to overtake, and wave goodbye to them. That’s just a statement of fact, which is kind of childish but, there is a lot of smirking going on in this room. (PTW 8)
Comparing groups on the themes they identified, bikers talk about very similar themes as drivers. However they additionally identify desirable states when motorcycling, such as being in ‘the groove’. Furthermore, because of their particular vulnerability, they are more expressive of the importance of maintaining concentration and of consciously maintaining an adequate safety margin which, for example, minimises the requirement for excessive braking. What are the implications of these findings for identification of themes for possible media campaigns? Bikers appear to be aware of the relationship between vehicle speed and risk of collision and severity of consequences. They appear to be aware that many factors can increase task difficulty or decrease their capability. They seem to be aware that there are many conditions that can raise their risk thresholds, as well as lower them. And they seem to be aware of influences on their disposition to conform to speed limits. At times they are motivated to exceed these by excessive
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amounts, but exploit this possibility in the context of an otherwise low task difficulty and a low probability of being detected and penalised. At other times they marginally exceed limits as a natural adjustment to a limit that may be a legal requirement but is otherwise a rather imprecise guide as to what the safe upper limit actually is at that moment in time. Thus there appears to be little of a knowledge gap that a media campaign might aim to influence. However, one possibility that might be targeted by a media campaign is the potentially dangerous influence of mood state on speed choice. We know far more today about how mood state can be managed, and this knowledge might usefully be passed on to bikers to help minimise the negative influence of mood state on decision making. A second possibility has to do with the establishment and influence of perceived norms. Inevitably riders’ attention is captured by marked deviations rather than the norm, but such selective attention may lead to the development of an inappropriate bias in the rider’s perceived norm. If riders are influenced in the direction of conforming to perceived norms (and there is evidence that drivers are so influenced; for example, Åberg et al., 1997), this bias can lower the quality of the rider’s behaviour and that in turn may feedback into contributing to a further decline in the actual norm. The consequence is a slow deterioration in the underlying riding culture of the community. Media campaign presentation of appropriate actual norms might help counteract this process, indicating for example that by far and away the majority of riders do not speed excessively, do not overtake dangerously and are not aggressive or competitive in their interaction with other road users. All groups identified time pressure as a key motive influencing increased speeds. Providing advice on time management to avoid this influence might be productive. On the other hand, counteracting the use of speed to obtain an adrenaline high may be much more intractable. An emergent theme that was frequently visited was the inherently social nature of riding: social in terms of perceived norms of behaviour, including speeding; social in terms of temporary ‘skirmishes’ and social in terms of the opportunities for status in bike ownership, style of riding and competitive engagement. These social variables are of interest because they not only drive up speed and risk thresholds but they are essentially independent of rider mobility goals. Perhaps then it should be in this area of inculcating more socially acceptable behaviour that a media campaign might focus. Acknowledgements This work was completed in part fulfilment of Department for Transport contract Number PPRO 4/001/015 (‘Improved Driver Information on Speed and Accident Risk’) and does not necessarily reflect the views of the Department. The researchers are indebted to the very cooperative participation of the focus group members and would like also to express their thanks to Alan Fisher and his staff for their generous assistance in enabling access to the Speed Awareness Course participants.
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References Aarts, L., and Van Schagen, I. (2006). ‘Driving speed and the risk of road crashes: a review.’ Accident Analysis and Prevention, 38, 215–24. Åberg, L., Larsen, L., Glad, A. and Beilinsson, L. (1997). ‘Observed vehicle speed and drivers’ perceived speed of others.’ Applied Psychology: an International Review, 46, 287–302. Aroni, R., Goeman, D., Stewart, K., Sawyer, S., Abramson, M. and Thien, F. (1999). Concepts of Rigour: When Methodological, Clinical and Ethical Interests Interact. Paper presented at the First AQR Conference, Melbourne, Australia, Retrieved February 9, 2007, from http://wwwlatrobe.edu.au/aqr/offer/papers/RAroni.htm. Baughan, C., Sexton, B. and Elliott, M. (2004). ‘Motorcyclists’ accident risk: results from a new survey.’ In Behavioural Research in Road Safety XIV. Department for Transport, London, 21–37. Bina, M., Graziano, F. and Bonino, S. (2006). ‘Risky driving and lifestyles in adolescence.’ Accident Analysis and Prevention, 38, 472–81. Clarke, D.D., Ward, P., Truman, W. and Bartle, C. (2004). ‘An in-depth case study of motorcycle accidents using police road accident files.’ In Behavioural Research in Road Safety XIV, Department for Transport, London, 5–20. Daly, J., Kelleher, A. and Gliksman, M. (1997). The Public Health Researcher: A Methodological Approach. Oxford University Press, Melbourne, Australia. Denzin, N.K. and Lincoln, Y.S. (1994). Handbook of Qualitative Research. Sage, Thousand Oaks, CA. Elliott, M.A., Sexton, B. and Keating, S. (2003). ‘Motorcyclists’ behaviour and accidents.’ In Behavioural Research in Road Safety XIII. Department for Transport, London, 139–52. Federal Highway Administration (1998). Synthesis of Safety Research Related to Speed. Publication No. FHWA-RD-98-154, Federal Highway Administration, Washington, DC. Fuller, R., Bates, H., Gormley, M., Hannigan, B., Stradling, S., Broughton, P., Kinnear, N. and O’Dolan, C. (2006). ‘Inappropriate high speed: who does it and why?’ In Behavioural Research in Road Safety 2006. Sixteenth Seminar. Department for Transport, London, 70–84. Fuller, R., Bates, H., Gormley, M., Hannigan, B., Stradling, S., Broughton, P., Kinnear, N. and O’Dolan, C. (2007). The Conditions for Inappropriate High Speed: A Review of the Research Literature from 1995 to 2006. Report under Contract Number PPRO 4/001/015 Improved Driver Information on Speed / Accident Risk (T201G). Department for Transport, London. Fuller, R. and Santos, J.A. (2002). Human Factors for Highway Engineers. Pergamon, Oxford. Koch, T. (1994). ‘Establishing rigour in qualitative research: The decision trail.’ Journal of Advanced Nursing, 19, 976–86. Morgan, D.L. (1997). Focus Groups as Qualitative Research (2nd ed.). Sage, Newbury Park, CA. Rice, P. and Ezzy, D. (1999). Qualitative research and evaluation methods (3rd ed.)
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Sage, Thousand Oaks, CA. Stradling, S., Gormley, M., Fuller, R., Broughton, P., Kinnear, N., O’Dolan, C. and Hannigan, B. (2007). ‘A typology of speeding drivers: extent of and motives for exceeding the speed limit.’ In Behavioural Research in Road Safety 2007. Seventeenth Seminar. Department for Transport, London, in press.
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Index
Note: Bold page numbers indicate illustrations; italic page numbers indicate tables and graphs. a2om Ltd 4, 32, 35 AAC see Early Driver Training Aberg, L. 202 ABS 291, 381 absenteeism 167-172 accelerometer 19 accident risk 76-77 age factor in 133, 277 and cognitive competence 253 and physical activity 167-172 self-reporting of 168 study method 168-169 study results 169-170, 170 action selection 252-253 ADHD 99, 102 Adolescent Road User Behaviour Questionnaire (ARBQ) 67 adolescents and PTWs 399-401, 425 advanced driving training 17, 190, 436 advertising campaigns 228 age factors 76, 108, 114 in accidents/speeding 133, 277 in fleet drivers 237, 242-243, 242 and hazard perception 132, 135, 136 in navigation system usage 292, 303, 306 older drivers 129-137 in accidents 129 cognitive abilities of 129-130 driving performance of 131 self-reporting by 130 and passengers 3 and self-reporting 131-132 and stress 129-137 coping strategies 133-137, 134, 135 study method for 131-132 study results for 132-134 transactional model for 137 in traffic offences 129, 132 in vigilance 373, 374, 374
aggression 3, 8, 10-11, 20, 77, 94-96, 117126 age/gender variables in 122, 123, 126, 129, 132, 135, 136 in DBQ 202 defined 94 in fleet drivers 236, 240, 240, 242, 242 and intent 118, 121, 122, 124-125, 125 in personality studies 52, 53, 57, 66, 91, 93, 94, 99 self-reporting of 123-124, 132 and speeding 124, 124 study method for 119-120 study results for 121-126 typology of 117, 121, 122, 124-125, 124, 125, 126 see also stress airbags 382-383 AISS (Arnett’s Inventory of Sensation Seeking) 68-70 alcohol see drink driving Alexander, J.L. 330 Allen, R.W. 265 Alverson-Eiland, L.G. 93, 99, 101 anger 91, 93, 94-96 measurement of see DAS; PADS and risky behaviour 107, 344, 427 trait see trait anger anticipatory driving 340-341 António, P. 401 anxiety 92, 93, 97, 99, 417 Appel, C. 93 appraisal theory 100 ARBQ (Adolescent Road User Behaviour Questionnaire) 67 ARCES (attention-related cognitive errors scale) 304 Arnett, J. 66, 68, 94, 102 assaultiveness 56, 57 attention, sustained 301-302, 304
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attitude see personality/attitude attributional theory 118 Australia 51-60, 75, 132 crash factors in 230 crash investigation in 379-388 DBQ study in 203-212 driver education study in 155-164 fleet safety study in 227-233 PBT study in 66 risky behaviour study in 175-185 rural roads in 369-370, 370 vigilance study in 367-375 work-related traffic injuries in 227-228 Austria, ISA study in 311-320 automation 252, 291 reliance/complacency 302-303, 304-305 see also navigation systems BAC (blood alcohol concentration) 327, 341, 380 Bañuls Egeda, R. 97 Barkley adult attention scale 79 Barkley, R.A. 79, 83 Bartle, C. 419 Bartlett’s sphericity test 240 Baughan, C.J. 67 Baxter, J. 129, 144, 327 Beirness, D.J. 52 Bell, P. 95 Ben-Yaacov, A. 326 benchmarking 227 Berg, H.Y. 60 Bergeron, J. 118 Biggs, H.C. 176 Bisantz, A.M. 304-305 Björnstig, U. 143 Blanchard, E.B. 118 Blomkvist, A. 93 blood alcohol concentration (BAC) 327, 341, 380 booster seats 393, 394, 396 Boud, A. 34 brake lights 326 braking 18, 20, 22, 27, 291 Britain 94, 311, 419, 429 driving instructors in 31-32 driving test in 31 injury/death rates for 391, 399, 415 Brno 312, 313
Brown, K.W. 304 Brown, L.B. 370 Burnett, G.E. 293-294 Burns, N. 66, 72 bus drivers 167-172, 240, 257 celeration behaviour of 190-196 Bylund, P.O. 143 caffeine 330 Caird, K.J. 143 Cairns, David 390 calibration of skills 38 Campbell, K. 129, 144, 327 Canada 75-85, 271-272, 295 capability 418-419, 418, 420-421 perceived 337, 338-339, 338, 342, 345346, 426, 427 car ownership 394-395, 394 Carbonell Vaya, E. 97 Carcary, B. 132, 137 Carmichael, S. 8 Carney, C. 20, 27 CARRS-Q 229, 233 Casanoves, M. 97 CBR (Dutch driving test organisation) 37 CBT (computer-based training/tests) 31, 252, 258, 259-262 CDC (Center for Disease Control) 266-267 CFQ (cognitive failure questionnaire) 304 chain of responsibility (COR) 228 challenge based enjoyment 415-416, 419, 420-421 change see stages of change change detection 252-253 Chapman, P. 96-97, 99, 102, 288 Charlton, S.G. 351 children and aggression 118 booster seats for 393, 394 and road safety 65-66 seat belt laws for 389-396 need for 391, 395 objective of 390-391 study method for 392 study results for 392-394, 392-394 Chisvert, M. 97 Christmas, S. 11 Cito Drive 252, 256, 260 Clarke, D.D. 419
Index Clarke, S. 144 classroom based training 18 Clegg, B.A. 33 close following 68, 179, 180, 180, 182, 183, 211, 327 countermeasures for 326 and other risk factors 331, 331 coach drivers 167-168 see also bus drivers cognitive failure questionnaire (CFQ) 304 cognitive functions 18, 38, 46, 76, 77, 97, 235-236 age factor in 129, 130, 277 and flow 417-418 and GPS usage 303, 304 and Groeger’s model 253-256, 254 and training 252-253 see also neurological factors Cohen, J. 54 commuter drivers 239, 242, 245 complacency 302-303, 304, 305 computer-based training/tests see CBT concentration, loss of 107, 108, 110, 111, 112, 113, 432, 438 Connors’ Continuous Performance Test (CPT-II) 78 control, loss of 107, 108, 110, 111, 112, 113 Cook, M.L. 265 COR (chain of responsibility) 228 Craen, S. 408 crash investigation 379-388 case study 382-383 computer modelling in 381 contributary factors data 383-385, 385 driver interviews 380, 383 local review panels 379-380, 381-382, 383, 385, 387 recommendations of 386, 388 site inspections 381, 383, 384, 386 study method for 380-383 study objectives 386-388 study outcomes 383-388 study participants 380, 382 vehicle inspection 380-381 crash records 17, 27, 51, 76, 114-115, 311 crash risk 3, 17 crashes costs of 175, 203, 227 factors in 325-332, 383-385, 385
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alcohol 327, 331, 331, 332 close following 326, 331, 331 correlations between 330-332, 331 fatigue 328-331, 332 hazard perception 330, 331, 332 speed 325-326, 331, 331, 332 violations 327-328, 331, 332 work-related 329 and human factor research 76-77 injuries/deaths in 65, 391, 415 novice drivers in 35, 37, 40, 51, 75 and driving exposure 51, 52-53, 60 environmental factor 51 inadequate skills factor 37, 38-39, 252-253 personality/attitudinal factor 51-60, 76-77 situation awareness factor 255-256 in post-training performance 20-22, 22, 23, 26 predictability of 75-76 self-reporting 52 work-related factors in, see also professional drivers crashes novice drivers in, age-related factor in 37-38, 39 Crick, J. 17 cruise control 291 Crundall, D. 96-97, 99, 102 Csikszentmihalyi, M. 416-418 cyclists 65-66, 176 Czech Republic, ISA study in 311-320 Dahlen, E.R. 107, 114 DAQ (Driver Attitudes Questionnaire) 67, 68, 175, 176, 177, 178, 180, 181182 DAS (Driving Anger Scale) 107-115, 175 reliability of 107-108, 110, 114 study method for 109-110 study results for 110-111 variables in 108, 111, 111, 112 Dawson, D. 371 DAX (Driving Anger Expression Inventory) 120, 123 DBI (Driver Behaviour Inventory) 239 DBQ (Driving Behaviour Questionnaire) 18, 109, 131, 135, 144, 145-146, 175176, 177, 178, 180, 181-182
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aggressive violation in 202, 211 errors/violations in 201, 202, 211, 314 fatigue in 203, 204, 211-212 modifications for fleets 201-212 factor structure/reliability 205-208, 205, 208, 210-211 list of items 204-205 predictive value 209 study method for 203-205 study results for 205-209 Positive Driver Behaviours in 145 range of uses for 201, 202 DCQ (Driving Coping Questionnaire) 131, 132, 236, 238 de Craen, S. 420 Deane, F.P. 98-99 defensive driving 17, 267 Deffenbacher, D.M. 126 Deffenbacher, J.L. 94-95, 99-100, 109-110, 114, 126 Dehaene, S. 371 DeKock, A.R. 92 Demick, J. 93, 101 DePasquale, J.P. 108 Desmond, P. 132, 137, 302 DfT (Dept for Transport) 291-292, 300, 390, 415 Dietrich, A. 417 Dimmer, A.R. 203 Dodge, K.A. 118 Dodson, J.D. 342 Dorn, L. 100, 101, 167, 171 Dorrian, J. 371 drink driving 3, 9, 13, 20, 54, 66, 75, 76, 80, 84, 176, 182, 341 blood alcohol concentration (BAC) 327, 341, 380 countermeasures for 327 and moped riders 404, 405 and other risk factors 331, 331, 332 and pre-drivers 68, 69-70, 72 strategies/interventions for 228 Driver Attitudes Questionnaire see DAQ Driver Behaviour Inventory (DBI) 239 driver celeration 189-196 habitual nature of 190, 194, 195 measurement of 191-192 monitoring required for 196 study method for 191-193, 195
study results for 193-194, 193, 194, 195-196 driver confidence 241, 241, 242, 243-244, 245 driver education see under professional drivers driver errors 17-18, 92, 93-94, 132, 135-136 distinct from violations 144, 145, 201, 327 of fleet drivers 179, 180, 181, 182, 184, 212 and vigilance 371, 372, 374, 375 driver improvement training 17 Driver Intervention Programme 53 Driver Social Desirability Scale (DSDS) 237, 243 Driver Stress Inventory see DSI driver training and celeration see driver celeration initial/post-licence 251-252 and predicting safe driving 189-196 simulation see simulation training/testing see also Task-Difficulty Homeostasis Model driver training intervention 231 evaluating 17-28 errors/weakness in 17-18 see also telemetric data tracking holistic 18 video trial 20, 27 DRIVERS database 54 Driving Anger Expression Inventory (DAX) 120, 123 Driving Anger Scale see DAS driving aspiration 6, 10-11, 13 Driving Behaviour Inventory (DBI) 98 Driving Behaviour Questionnaire see DBQ driving distance 24, 27 driving experience 38, 39 driving exposure 51, 52-53, 55, 56-58, 5960, 59, 71 age factor in 130 and anger 110, 111 of professional drivers 181, 182, 182, 183-184, 207-209, 212 driving, fear/dislike of 99, 100, 132, 134, 134, 236, 245 Driving Instructor Checklist 78-79
Index driving instructors 33-35 core competencies of 31-32 as evaluators 78-79, 80, 84-85, 101, 189-190 and independent driving 45 and self-assessment 35 Driving Instructors’ Handbook (2006) 33 driving lessons 32-36 and hazard perception 47 and independent driving 45 learning curve in 33-34, 34 not true driving environment 32-33, 39 and self-assessment 34-35 see also driver training driving licences 53 driving logs 96, 97 driving simulators 101, 368 Driving Skills Inventory (DSI) 175, 216, 217, 220 driving speed 23, 24, 26 see also speeding Driving Standards Agency (DSA) 31 driving tests 31, 39-40 fault-based assessment in 31-32, 33, 35 multimedia 251-262 practical 37-48 changes to 37, 48 examiners’ questions in 47, 48 hazard perception/management element 40, 42, 44, 47 independent driving element 40-41, 44-46, 45, 47-48 Learner Interim 43(n2), 44, 45 ‘productive’ special manoeuvres element 40, 41-42, 45, 46, 48 self reflection element 40, 42-43, 44, 47, 48 study method 43-44 study participants 44 study results 44-47 as predictors of safe driving 189-191 shortcomings of 189-190 three testing methods 189 Wiener Fahrprobe 311, 312-314 see also responsive driving; Task-Difficulty Homeostasis Model drugs 66, 81-82, 82-83, 84, 341, 386, 400 Drummond, A.E. 252 Drury, C.G. 304-305
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DSDS (Driver Social Desirability Scale) 237, 243 DSI (Driver Stress Inventory) 131, 132, 134, 135, 136, 236, 238 Early Driver Training (AAC) 277-278 eating while driving 204 EcoDriving 191 educational factors 76, 408 Edwards, I. 32 Ellingstad, V.S. 92 Elliott, M.A. 67 Ellison-Potter, P. 95 emotions and road user behaviour 91-102, 331, 331, 341 aggression/risky behaviour studies 92, 94-96 concepts/frameworks in studies 99-100, 102 and driver training 342 errors/violations studies 92, 93-94 and fatigue 332 key terms 91 measurement of 101, 102 personality studies 98-99 road safety/accidents studies 92, 96-97, 101 self-reporting in studies 94, 102 situational characteristics studies 98, 99 speeding 342, 343-344 task performance studies 92-93 see also mood Endsley, M.R. 255-256 Engström, I. 31 enjoyment 415-418 challenge based 415-416, 419, 420-421 and flow 416-418, 417 rush based 415, 416, 419, 420, 421, 433-434 see also sensation seeking EPI (Eysenck Personality Inventory) 98 EPQR (Eysenck Personality Questionnaire) 370, 375 Evans, B.L. 351 Evans, J. 99 Evans, L. 215, 326 eye scanning 18 Eysenck Personality Inventory (EPI) 98
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Eysenck Personality Questionnaire (EPQR) 370, 375 FA (factor analysis) 238-239 fathers, and sons’ driving behaviour 3, 6-8 fatigue 32, 132, 134, 134, 184, 328-330, 383, 432, 437 countermeasures for 330 in DBQ 203, 204, 211-212 and emotion 332 of fleet drivers 229, 230, 235, 236, 237, 240, 243, 245 measuring 328-330 need for recovery from 167-172 fault-based assessment 31-32, 33, 35 FDRI (fleet driver risk index) 241, 242, 244, 246 Field, A. 239 Fine, M.A. 94 Finland 42, 94, 143 Fisher, D.A. 288 FJC programme see moped riders Flannagan, M. 326 fleets/fleet drivers 175-185 age factors 237, 242-243, 242 aggression in 236, 240, 240, 242, 242 and BDQ 202-203 crash factors in 230, 235 crash types in 229-230 data collection approaches in 229-231 driver behaviour factors 229, 229 gender differences in 242, 242 OHS in 228 risk assessment of 235-246 factor analysis 237-241, 240 FDRI 241, 242, 244, 246 validity analysis 237, 241-245 violations/crash involvement 244245 safety culture of 180, 181, 183, 184-185, 227-233 baseline measures 232-233 driver training /education 231, 236-237 needs analysis 232 safety climate 231-232 self-assessment by 237 and speeding 176, 179, 180, 182, 183, 210, 232
and stress 235-237 coping strategies 236, 237, 240-241, 241, 242-243, 244, 245 time pressure 235, 240 thrill seeking in 236, 240, 240, 242 Fletcher, J. 419 flow theory 416-418, 417 following distance 326, 403 Forbes, N. 293-294 Ford, F.H. 93, 99, 101 Forward, S.E. 4 France 399 Early Driver Training in 277-278 Francis, L.J. 370 Friedenberg, B.M. 118 friends, influence driving behaviour 3, 8-9 Frings-Dresen, M.H. 167-168, 171 frustration-aggression hypothesis 100 fuel-efficient driving 191 Fuller, R. 349, 352, 353, 362, 363, 418 g-force 19, 20, 21-22, 27 GADGET project 39 Garrity, R.D. 93, 101 GDE (Goals for Driver Education) 14, 18, 37 hierarchical matrix in 31, 33, 34, 35, 3940, 47, 251-252 GDL (graduated driver licencing) 266 gear-changing 287 gender factors in aggression 122, 123, 126, 129, 132, 135, 136 in coping strategies 133 in fleet drivers 242, 242 in lapses/errors 132, 135-136, 374-375 in navigation system use 292, 300-301 in novice drivers 38, 52, 55, 59, 60, 69, 76, 114 accident rates 277 and passengers 3 genetics of driving behaviour 176 Gerbers, M.A. 327 GHQ (General Health Questionnaire) 168, 169 Gilliland, K. 132, 137 Glendon, A.I. 12 Goals for Driver Education see GDE Goldberg, A.I. 151
Index Goldenbeld, C. 408, 420 Goodman, M. 305 GPS see navigation systems graduated driver licencing (GDL) 266 Greenhouse-Geisser statistic 360 Gregersen, N.P. 60, 236-237, 330 Gregor, S. 304 Groeger, J.A. 33, 92 Groeger’s four facet model 253-256, 254, 259 GRSW (Global Road Safety Week) 65 Guadagnoli, E. 238 guilt/shame 435-436, 437 Gulian, E. 100, 137 Haigh, K.Z. 303 Haigney, D. 129 Hall, J. 78 Hamilton, K. 419 hard braking 20, 22, 27 Harvey, C.F. 326 Hattaka, M. 31 hazard perception 18, 27, 31, 37, 40, 42, 47, 132, 135, 136, 236 countermeasures for 330 driver education and 155 and other risk factors 331, 332 and simulators 265 health & safety see OHS Heavy EcoDriving 191 Heimstra, N.W. 92 Hewson, C. 300 higher order skills 35, 37, 38, 39, 44 Hiles, J. 371 Ho, G. 303 Hobbes, G.E. 77 Hofmann, D.A. 151 horn-honking 91 Horne, J. 328 Horswill, M. 330 Horta, M. 401 Huberman, A.M. 158 Human Information Processing Model 371 impression management 237, 241, 241, 243244, 245 in-car devices see ISA; navigation systems Inagaki, T. 305 inattention 77, 78
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independent driving 40-41, 44-46, 45, 47-48 defined 41 information processing 349 see also cognitive functions; neurological factors insurance 175, 177, 231 Internet questionnaires 294, 300-301 IPAQ (International Physical Activity Questionnaire) 168, 169, 170, 172 IRT (item response theory) models 261-262 ISA (intelligent speed adaptation) 311-320, 326, 386 attitudes towards 317-319, 319, 320 and pedestrians 311 and speeding 312, 317, 318, 319, 345 study method for 311-315 ISA questionnaire 314-315 psychological group training 315, 320 use of Wiener Fahrprobe 311, 312314 study, participants in 312, 319-320 study results for 315-319 self-reported 315-317, 316 ISH (inappropriate high speed) see speeding Italy 399 item response theory, see also IRT Iversen, H. 72 Janke, M.K. 271, 271-274 Japan 195 J.D.Power survey 292-293, 300 JDQ (Jerome driving questionnaire) 79, 81 Jenkins, D. 326 Jerome, L. 77, 80 Jessor, R. 66, 72 Jian, J.J. 304-305 Joint, M. 100 Joyner, L. 132, 137, 302 Kaiser-Meyer-Olkin test 240 Kiff, L.M. 303 Kipling, J. 420 Kirschenbaum, A. 151 Kline, P. 239 Kline, T.J. 143 Knee, C.R. 95-96, 100 Kölner Fahrverhaltens test 313 Krupat, E. 330
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Ladyman, Stephen 390 Lähdeniemi, E. 143 Lajunen, T. 94, 130, 202 Lamond, N. 371 Larsson, T.J. 143 Lawton, R. 93-94, 99, 100, 201 learning 3-4 situational 3 and social processes 4 Lee, J.D. 20, 27 Lee, T.R. 144 Levelt, P.B.M. 97 log books 17 Loh, S. 371 lorry drivers 97, 257 Lynch, R.S. 94-95, 114, 126 MAAS (mindful attention awareness scale) 304 Macdonald, L. 130 McGehee, D.V. 20, 27 McKenna, F.P. 17, 328, 330, 332 Mackworth, N.H. 371 Madsen, M. 304 Malta, L.S. 98, 117-118 Malz, M. 326 Manchester Driving Behaviour Questionnaire see DBQ Mann, R.E. 327 manoeuvring 18, 31 Manstead, A.S.R. 68, 93, 129, 144, 201, 327 Masten, S.V. 271, 271-274 Matos, M. 401 Matthews, G. 100, 101, 132, 137, 302 Mattila, M. 144 Mayhew, D.R. 271-272, 271-274 Meadows, M. 202 Mesken, T. 202 Mesken, J. 253 meta-cognitive skills 38 Meuter, R.F. 371 Miles, M.B. 158 Miller, J. 33 mindful attention awareness scale (MAAS) 304 Mitsopoulos, E. 3 mobile phones 76, 203, 204, 205, 328 modelling behaviour 6-9 Molloy, R. 305
Monash University Accident Research Centre see MUARC Monteagudo, M.J. 97 mood and driving performance 92-93, 98, 102, 404, 405, 410 see also emotion moped riders 399-410 accident rates of 399, 404-406, 405, 407, 408 socio-economic factors in 407, 408, 409 adolescents as 399-401 family background 406, 407, 408, 409 study method for 401-402 study results for 402-408 and suicide risk 399, 400, 401, 402, 406-409, 407, 410 Moray, N. 305 Morgan, D.L. 429 mothers, and children’s driving 7, 8 motorcyclists 93, 257, 312, 340, 353, 381 in crashes 425 on bends 419, 425 mortality rates 399, 404-406, 405, 415 while overtaking 419-420 rider motivation 415-418, 437 enjoyment and flow 416-418, 417 enjoyment types 415-416, 419, 420, 421, 431-432, 433-434 and speed limits 435-437 speeding and 425-440 and bike characteristics 432-433, 434 competitiveness factor 434, 438 countermeasures for 438-439 discussion themes in study 429-430, 430 distal/proximal factors 433-435 research process in study 429-430, 431 and road characteristics 432 study aims 428 study method 428-430 study results 430-437 time-saving factor 435 and weather 433, 437
Index and task demand/difficulty 417, 418421, 418 factors influencing 432-435 task-difficulty homeostasis 426-427, 426, 427, 430-432, 437 training 415-421 skills/behavioural 420-421 see also mopeds Mouloua, M. 303 MUARC (Monash University Accident Research Centre) 379, 380, 381, 382, 388 Mulder, M. 256-257 multiple-vehicle crashes 129 multitasking 76, 203, 204-205, 210 Murray, W. 176, 227 Nada-Raja, S. 83 Nagin, D.S. 328 navigation systems 19, 27, 44, 45, 45, 291306 distraction caused by 292 future research on 303-306 attenion/trust/complacency tests 304-305 map use/updating on 297, 297, 300, 301 misdirection by 293, 295-297, 296, 298299, 301-303 and sustained attention 301-302, 304 over-reliance on 301-303, 304-305 study method for 294 study results for 295-299, 295-300 surveys of 291-294, 300 user demographics 292, 295, 295, 300301, 306 near accidents 92, 96-97, 101, 130 and anger 107, 108, 110, 111, 112, 113 Neighbors, C. 95-96, 100 Netherlands 94 periodic Road Safety Surveys in 39 practical driving test in 37-48 responsive driving tests in 251-262 neurological factors 349, 363, 417-418 see also cognitive functions New Zealand 67-72, 75, 156, 351 angry driving study in 109 telemetric data tracking study in 17-28 Newman, S. 176
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NFOVD simulator 265, 266, 269, 271-274, 273 night cruises 8-9 nightshift working 329, 332 non-driving road users 65-67 vulnerability of 65-66 Northern Ireland 389-396 injury/death figures for 391 novice drivers see young drivers Nutter, A. 99 oblique rotation 239 occupational factors 76 OECD Young Driver report (2006) 3, 32 Offer, D. 94 OHS (occupational health & safety) 176177, 184-185, 227 Oigenblick, L. 151 one-parameter logistic model (OPLM) 261 Ontario 75-85 OPLM see one-parameter logistic model Osberg, T.M. 237 other-blame 100 overtaking 68, 176, 180-181, 180, 182, 183, 236 and ISA 317 in simulator training 283, 283, 286 PADS (Propensity for Angry Driving Scale) 107-115 reliability of 107-108, 110, 114 study method for 109-110 study results for 110-111 variables in 108, 111, 111, 112 Pajo, K.B. 202 Pak, A. 271-272, 271-274 Parasuraman, R. 302, 305 parents 400, 406 and child seat belt laws 392-394, 392394, 395 and drink driving 9 fathers/sons’ driving behaviour 3, 6-8 influence on children’s driving 3, 6-9, 13, 77 distancing behaviour 10 mothers and children’s driving 7, 8 and road safety 66 unsafe driving by 13 Park, G.D. 265
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Parker, D. 32, 68, 93, 94, 100, 130, 132, 201, 202, 203, 327 parking 18, 41-42, 46, 46 Parkinson, B. 99, 100 passengers influence driving behaviour 3, 12, 117, 344 see also children Pastor, G. 97 PBT (Problem Behaviour Theory) 66, 72, 77 PCA (principal components analysis) 238240, 243 Peck, R.C. 327 pedestrians 66, 67, 95 and ISA 311 in simulator training 265, 268, 279, 280, 281, 286, 287 peer groups 3, 8-9, 38, 77 and road safety 66 peer pressure 11-12, 32 Pelz, D.C. 330 personality profiles 370 personality/attitude 51-60 and aggression 117-118 and non-driving road users 66 of pre-drivers 67 in predicting risk 75-77 and traffic offences 51-60 and driving exposure 51, 52-53, 55, 56-58, 59-60, 59 limitations of research 52 statistical analysis 54 study method 53-54 study, participants in 53 study results 54-58 types 52, 66, 68 see also aggression; psychopathology Persson, L. 93 Philipchalk, R. 370 planned behaviour theory see TPB Plocher, T. 303 Podd, J.V. 98-99 Pogarsky, G. 328 police drivers 240 POMS (Profile of Mood States) 93 Portugal, moped study in 399-410 pre-driving attitudes 65, 67 interventions in 71 study method 67-69
study results 69-71 pre-licence training 17 principal components analysis see PCA Privilege Insurance 292 Problem Behaviour Theory see PBT problem driving risk 75-85 predictability of 75-77, 83-85 screening instruments 78-79, 85 study method 77-78 study results 80-83 professional drivers 97, 143-151 accident risk of 143, 149-151, 167-168, 169, 170, 175, 202-203, 227-228 costs of crashes by 175, 203, 227 DBQ and 202-203 driver behaviours of 144-145 error/violation rates 146-149 positive 145, 146, 148, 149 risky see under risky behaviour driver education for 155-164 effectiveness of 155 and stages of change model 155157, 162-163 study method 157-158 study results 158-162 and physical activity 167-172 self-reporting of 168 study method 168-169 study results 169-170, 170 safety culture of 144, 149-151 measurement scale 146, 147 regression analysis 149, 150 study method 145-146 study results 146-149, 147-148 see also fleets/fleet drivers Profile of Mood States (POMS) 93 Propensity for Angry Driving Scale see PADS psychometrics 32, 107-108, 314 psychopathology 399, 400, 401, 402, 406409, 407, 410 PTW (powered two-wheelers) see moped riders; motorcyclists PVT (Psychometer Vigilance Task) 367, 368-369, 371 qualitative/quantative research 428 Quenault’s driving test 313 Quimby, A.R. 330
Index Rabbitt, P. 129, 135 Raby, M. 20, 27 Rachman theory 100 Ragan, K.M. 107, 114 RBQ (Road Behaviour Questionnaire) 401, 402 reaction speed 254 reactive driving 340-341 rear-view mirror 287, 403, 404 Reason, J.T. 93, 144, 149, 201, 327 Regan, M.A. 3 Regional Development Strategy 395 reliance 302 responsive driving, training/testing 251-262 CBT in 252, 258, 259-262 change detection/action selection 252253, 256, 259 competence assessment 256-258 constituents of 256-257 eclectic model of 257, 258 methods of 258 purposes of 257-258 data analysis in 258, 261-262 and Groeger’s model 253-256, 254 instruments used in 260-262 item selection 260-261 participants in 261 and situation awareness 255-256 Reyes, M.L. 20, 27 Reyner, L. 328 Richards, T.L. 94-95, 126 Rimmo, P. 202 risk awareness/perception 251, 352, 353 driver’s feeling of 349-363 and loss of control probability 361, 361, 363 and maximum speed 362 safety margin 350, 363 study method for 354-356 study, participants in 355 study results for 356-362 and task difficulty 350-353, 351, 352, 356-357, 357, 358, 359360, 359, 362 and TCI 349, 350, 352 management 18, 158, 159, 161, 162-163 threshold 426-427, 426, 427, 433-434 risky behaviour 3, 8-9, 12, 18, 27
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and adolescents 400, 403, 403, 404 and anger 107, 113, 118, 344 countermeasures for 345 defined 94-95 of non-driving road users 66 and norms 4, 12-13, 56 personality/attitudinal factor in 52, 54, 56, 56, 57, 344-345 and pre-drivers 67-68, 72 of professional drivers 175-185, 344 measurement tools for 175-177, 178, 179, 180-182, 180 self-reporting by 175, 176, 177, 181, 185 study implications/difficulties 184185 study method for 177-179 study results for 179-182 psychosocial models of 77 see also drink driving; speeding Roach, G. 371 Road Behaviour Questionnaire see RBQ road lighting 351, 385 road rage 99, 100 road safety 65-66, 67, 71, 96-97, 117 and ISA 312, 315, 318, 319 strategic partnerships in 387 work-related 180 change in 155-164 Road Safety Strategy 2004-6 345 road width 351 Roberts, K. 288 Roelofs, E.C. 257 Rosenthal, T.J. 265 Rothengatter, T. 295 rumble strips 330 Rundmo, T. 72 rural driving 26-27, 69, 178 and vigilance 369-370 rush based enjoyment 415, 416, 419, 420, 421, 433-434 rush hour 117 Ryan, R.M. 304 Safety Climate Questionnaire see SCQ-MD Salminen, S. 143 Sanders, P. 257 SART (sustained attention to response task) 302, 304
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satnav see navigation systems SCQ-MD (Safety Climate QuestionnaireMD) 175, 176, 177, 178, 180, 181-182 seat belts 67, 68, 69, 72, 76, 204-205 and booster seats 393, 394, 396 hazardous to children 395 laws 215, 224, 389-396 attitudes towards 394, 394 need for 391 objective of 390-391 study method for 392 study results for 392-394, 392-394 see also under taxi drivers Segal, A.U. 77, 80 self assessment 20, 21, 34-35, 39, 42-43, 47, 237, 251 self deception 237 see also driver confidence self-image 4, 18, 27, 32 self-reporting 52, 59, 60, 69, 72, 79, 83, 114, 123-124 Senior, V. 68 sensation seeking 52, 53, 66, 72, 76, 132, 133-134, 136, 327 in fleet drivers 236, 240, 240, 242 inventory of (AISS) 68-70 in motorcyclists 415-418, 417 in non-driving road users 66 and other risk factors 331, 331, 332 sex differences in 69, 71 see also enjoyment Sexton, B. 419 Shaffer, J.P. 239 Sheridan, T. 302 shift work 329, 332 Shinar, D. 326 Shrauger, S.J. 237 siblings, influence driving behaviour 8-9, 10-11 Sigman, N. 371 significant others and young drivers 3-14, 344 distancing behaviour 10-13 and drink driving 9 and driver education 13-14 modelling behaviour 6-9, 13 and risk taking 12, 13 as role models 4
study methods for 4-6 Simpson, H.M. 271-272, 271-274 simulation training/testing 265-275, 266, 351 and accident rates 265 of accident scenarios 277-288 response times 281-286, 281-284 study method for 278-280, 286 study results 280-286 driving scenarios in 267-268, 279-280 efficacy of 240, 266-267, 273-275, 287288, 305 and ISA 311 pedestrians in 265, 268, 279, 280, 281, 286, 287 performance measures in 268 study method 267-268 study, participants in 268, 269 study results 269-273, 270 traffic lights in 265, 267, 281, 282, 286, 287 vigilance test 368-375 simulator sickness 305 Singh, I.L. 305 Sinnett, E.R. 239 situation awareness 255-256, 304 situational learning 3 Sivak, M. 326 Sjöberg, L. 93 skid training 18, 93 sleep 328-330, 331 countermeasures for 330 disorders 328 see also fatigue sleepy driving 329, 330-331, 332 Sluiter, J.K. 167-168, 171 smoking 169 social cognition 4 social information processing theory 100 speed cameras 326, 328, 339 speed choice 351-353 and loss of control probability 361, 361, 363 and road width 351 safety margin 350, 363 study method for 354-356 study, participants in 355 study results for 356-362
Index and task difficulty 349, 351, 351, 356357, 357, 358, 359-360, 359, 362, 419-420 speed humps 326 speed limits 20, 23, 318, 319, 435-437 speeding 3, 6-8, 12, 37, 76, 190 and aggression 124, 124 countermeasures for 325-326 and emotion 94, 110, 332, 342, 343-344 and fleet drivers 176, 179, 180, 182, 183, 210, 232 and guilt/shame 435-436, 437 as immature behaviour 344-345, 425, 438 and ISA 312, 318, 319317 measuring 325 and norms 56, 57, 68, 70, 183 and other risk factors 331, 331, 332 in personality study 53, 56, 66, 68 and task-difficulty homeostasis 337-338, 338, 342 telemetric data tracking of 20, 22, 25, 27 and time-saving 343 TPB- 67, 68, 69, 70 see also driver celeration; and see under motorcyclists Spielberger, C. 99-100 Stacey, S. 33 stages of change model 155-157 five stages in 162-163 State Trait Anxiety Inventory 93 Stephens, A.N. 92 Stetzer, A. 151 stop-signal paradigm test 78 stopping manoeuvre 42 Stork, J. 401, 409 Stradling, S. 32, 68, 93, 94, 100, 129, 144, 201, 327, 343, 344 stress 32, 52, 91 age factor in see under age factors coping with 131, 132-137, 236 measurement of 132 strategies 133-137 training in 245 measuring 132 in professional/fleet drivers see under fleet drivers transactional models of 100, 235-236
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suicide risk 399, 400, 401, 402, 406-409, 407, 410 Sullman, M.J. 202, 203 Summala, H. 94, 130, 202 Sumner, R. 326 sustained attention 301-302, 304 Sutcliffe, P. 130 Svahn, F. 292, 293, 300 Sweden 42, 132, 143, 311 celeration study in 189-196 SWOV (Scientific Research of Traffic Safety) 252 Taieb, M. 326 tailgating 2, 120, 125, 236 task demand/difficulty 349-353, 350, 415416, 418 and motorcyclists 417, 418-421, 426427, 426, 427 Task-Difficulty Homeostasis Model 337346, 350-353 acceptable task difficulty in 338, 343 calibration problem 339-340 comparator in 351, 352, 353, 426 determinants in 338 driver competence in 338-339 human factor variables in 338, 341-342, 346 model described 337-339, 338 and motorcyclists 425, 426-427, 426427, 429, 430-432, 437 perceived capability in 337, 338-339, 338, 342, 345-346 perceived demands in 337, 338, 338, 339 and reactive/anticipatory driving 340341 risk threshold in 337, 338, 339, 343-345, 346 and simulator driving 340 and speed 337-338, 338, 342, 343 taxi drivers/taxis 97, 145 seat belt laws for 389, 390 seat belt study 215-224 private car use 217, 222, 222 reasons for not using 215, 216, 217220, 218-219, 221, 222-223 self-reporting in 224 study method 216-217
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study results 217-223 Taylor, A.H. 167, 171 Taylor, J.E. 98-99 TCI (Task Capability Interface) 349, 350, 352, 362-363 TCI (temperament & character inventory) 79 telemetric data tracking 18-28 map-based function 23, 27 participants in study 18-19 results 20-23, 24-26 study method 18-20 tracking system 19-20, 19, 23 tension 66, 77, 98 thematic analysis 428-429 three pathways theory 100 thrill seeking see sensation seeking Tillman, W.A. 77 time management 343, 439 time pressure 235, 240, 343, 435, 439 TNO 252 TPB (Theory of Planned Behaviour) 100, 120, 124 TPB-speeding 67, 68, 69, 70 traffic control devices 265, 267, 281, 282, 286, 287 traffic jams 97 traffic violations 109, 110, 331 age factor in 129, 132 and aggression 124, 124 countermeasures for 327-328 and driving exposure 55 and emotion/mood 93-94, 96, 101, 111, 111, 112, 114 and other risk factors 331, 331, 332 personality/attitudinal factors in 51-60, 55, 56-58, 57 and social norms 2 see also speeding trait anger 99-100, 102, 110 Scale (TAS) 109, 111, 111, 112, 114 Transport, Dept of, study (2007) 11 Transport Research Laboratory 39-40 Truman, W. 419 trust/over-trust 302-303, 304-305 Turkey, seat belt study in 215-224 turning manoeuvre 41, 46, 46 Twisk, D. 408, 420
Ulleberg, P. 72 UN 65 Underwood, D. 96-97 Underwood, G. 99, 102, 288 United States 75, 143, 295 GDL in 266 seat belt use in 215 simulator training in 265-275 Vallerand, R.J., 118 Vallières, E.F. 118 van der Beek, A.J. 167-168, 171 Varimax analysis 239, 240 Varonen, U. 144 vehicle control skills 18 Velicer, W.F. 238 Vienna 312, 313 Vietor, N.A. 95-96 vigilance 367-375 age factor in 370, 372, 372, 373-374, 374 and BMI 370, 372, 375 gender factor in 370, 372, 372, 373, 373, 374-375 low, impact of 371, 371 and performance metrics 371, 372-373, 374 reaction times, analysis of 372-373, 372, 373 study method 368-372 study results 372-374 subjective factors and 367, 370, 371, 373, 374 test (PVT) 301-302, 367, 368-369, 371 three factors of 367 Vincenzi, C. 303 Von Thaden, T.L. 144 Vuuren, W. 151 Ward, P. 419 Wasielewski, P. 326 Watson, B. 176 Watts, G.R. 330 weather conditions 51, 433, 437 Weiner, E.L. 302 Wellbrink, J. 371 West, R. 66, 72, 78 Westerman, S.J. 129
Index WFOVC/WFOVD simulator 265, 266, 269, 271, 271-274, 273-274 White, G.S. 114 Whitmore, 34 Wiegmann, D.A. 144 Wiener Fahrprobe method 311, 312-314 observation variables in 313-314 Wilde, Gerald 76 Williamson, A. 371 Wills, A.R. 176 work-related driving see professional drivers World Health Day (WHD) 65 World Health Organisation (WHO) 65 Wright, S. 96-97, 102 Wundersitz, L. 66, 72 Xie, C. 203 Yagil, D. 96 Yerkes, R.M. 342 Yerkes-Dodson Law 342 young drivers accident rates of 269-274, 270-274 coping strategies of 133-137, 135 in crashes 20-22, 27, 35, 37, 51, 265, 266-267
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deaths 65, 75 and Early Driver Training 277-278 gender factors see under gender factors and GRSW 65 post-training performance of 17-28 study method 18-20 study results 20-21, 24-26 tracking system 19-20, 19, 23 reactive responses of 340-341 risk-taking by 400, 403, 403, 404 and significant others see significant others in simulation training see simulation training/testing speeding by 27 see also mopeds YRBS (Youth risk behaviour surveillance system) 79, 81, 84 Zhang, H. 144 Zohar, D. 144 Zyda, M. 371 Zylman, R. 59