Travel Behaviour Research: Updating the State of Play

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TRAVEL BEHAVIOUR RESEARCH: UPDATING THE STATE OF PLAY

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TRAVEL BEHAVIOUR RESEARCH: UPDATING THE STATE OF PLAY

Edited by JUAN DE DIGS ORTUZAR Pontificia Universidad Catolica de Chile DAVID HENSHER University of Sydney and

SERGIO JARA-DIAZ Universidad de Chile

1998 ELSEVIER Amsterdam - Lausanne - New York - Oxford - Shannon - Singapore - Tokyo

ELSEVIER SCIENCE Ltd The Boulevard, Langford Lane Kidlington, Oxford, OX5 1GB, UK

Library of Congress Cataloging in Publication Data A catalog record from the Library of Congress has been applied for. British Library Cataloguing in Publication Data A catalogue record from the British Library has been applied for.

First edition 1998

ISBN: 0-08-043360-X

© 1998 Elsevier Science Ltd 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, electrostatic, magnetic tape, mechanical, photocopying, recording or otherwise, without permission in writing from the publishers. (=c) The paper used in this publication meets the requirements of ANSI/NISO Z39.48-1992 (Permanence of Paper). Printed in The Netherlands.

CONTENTS

Foreword

ix

Part I

Underpinnings of Travel Behaviour

Chapter 1

Behavioural Assumptions Overlooked in Travel Choice Modelling Tommy Gdrling

Chapter 2

Chapter 3

Chapter 4

Chapter 5

A General Micro-Model of Users' Behaviour: The Basic Issues Sergio R. Jam-Diaz

19

Causal Analysis in Travel Behaviour Research: A Cautionary Note Ram M. Pendyala

35

Driver Information Processing Failures in Road Accidents: From Description to Interpretation Pierre Van Elslande and Daniele Dubois

49

The Simulation of Behaviour in a Non-Experienced Future: The Case of Urban Road Pricing Charles Raux, Odile Andan and Cecile Godinot

67

Part II

Stated Preference

Chapter 6

Reflections on Stated Preference: Theory and Practice John Bates

Chapter 7

3

Stated Preference Studies: The Design Affects the Results Staffan Widlert

89

105

vi

Travel Behaviour Research: Updating the State of Play

Chapter 8

Chapter 9

Own Account or Hire Freight: A Stated Preference Analysis Lasse Fridstr0m and Anne Madslien

123

Behavioural Models of Airport Choice and Air Route Choice Mark A. Bradley

141

Chapter 10 A Model of Employee Participation in Telecommuting Programs Based on Stated Preference Data Jin-Ru Yen, Hani S. Mahmassani and Robert Hermann

161

Chapter 11 Discrete Logit Modelling Based on SP Data of the Analytic Hierarchy Process for Parking Choice Shoji Matsumoto and Luperfina E. Rajas

181

Part III

Travel Patterns

Chapter 12 Estimation of Origin-Destination Matrices Using Traffic Counts: An Application to Stockholm, Sweden Torgil Abrahamsson

199

Chapter 13 Two New Methods for Estimating Trip Matrices from Traffic Counts Otto Anker Nielsen

221

Chapter 14 Simple Models of Highway Reliability—Supply Effects Luis G. Willumsen and Nick B. Hounsell

251

Chapter 15 Detecting Long-Term Trends in Travel Behaviour: Problems Associated with Repeated National Personal Travel Surveys Uwe Kunert

263

Contents

vii

Chapter 16 Mobility Surveys in Lisbon and Porto: A Comparative Analysis of Results Jose Manuel Viegas and Faustina Guedes Gomes

279

Chapter 17 Changes in Urban Travel Behaviour of Elderly People Pascal Pocket

299

Chapter 18 Potential Estimate for the Acceptance of a New Motorised Bicycle in Urban Traffic: Methodic Aspects and Results Gerd Sammer, Kurt Fallast and Fritz Wernsperger

317

Chapter 19

Configurational Modelling of Urban Movement Networks Alan Penn, Bill Hillier, David Banister and Jianming Xu

339

Part IV

Dynamics of Route Choice

Chapter 20

Day-To-Day Dynamics of Urban Commuter Departure Time and Route Switching Decisions: Joint Model Estimation Rong-Chang Jou and Hani S. Mahmassani

365

Departure Time and Path Choice Models for Intercity Transit Assignment Agostino Nuzzolo and Francesco Russo

385

The Impact of Dynamic Traffic Information: Modelling Approach and Empirical Results Eric C. van Berkum and Peter H.J. van der Mede

401

Chapter 21

Chapter 22

Part V

Methodological Advancements

Chapter 23

Bayesian Reliability of Discrete Choice Models Rodrigo A. Garrido

425

viii


Chapter 24

Discrete Choice Models with Latent Variables using Subjective Data Takayuki Morikawa and Kuniaki Sasaki

435

Chapter 25 The Stability of Parameter Estimates in Household Based Structured Logit Models for Travel-to-Work Decisions Terje Tretvik and Staff an Widlert

457

Chapter 26 The Dependent Availability Logit Model and its Applications Wafaa Saleh and Michael G.H. Bell

473

Chapter 27

The Timing of Change for Automobile Transactions: Competing Risk Multispell Specification David A. Hensher

Chapter 28 Forecasting Car-Occupancy: Literature Review and Model Development Gerard De Jong, Andrew Daly, Hugh Gunn and Ursula Blom Chapter 29

Co-Ordination of Road Pricing Policies in Hong Kong William H.K. Lam and Rui J. Ye

487

507

527

List of Participants

541

Index

545

FOREWORD The Seventh International Conference on Travel Behaviour held in Valle Nevado, Santiago, Chile from 13-16 June 1994, hosted by the International Association of Travel Behaviour Research (IATBR), continued the commitment through the assembly of papers and participants to review the state of the art and practice in travel behaviour research. Previous conferences have been held since 1973 in South Berwick, Maine; Asheville, North Carolina; Tanunda, South Australia; Eibsee, Germany; Aixen-Provence, France; and Quebec, Canada. A total of 90 papers under 23 themes were presented in general sessions and workshops. Themes included estimation of OD matrices, car use modelling, freight modelling, stated preference methods, allocation and valuation of travel time savings, activity-based analysis, travel surveys, departure time and route choice, dynamic traffic information, behavioural policy issues such as road pricing, developments in land use and transport modelling, parking choice models, model estimation, longitudinal data and timing of change, and a range of applications in urban and interurban contexts in passenger and freight markets. Twenty-nine papers from the conference have been accepted after refereeing and revision for this book. In selecting papers the single criterion was excellence. The themes herein represent the latest published update by IATBR of the map of travel behaviour research, which in time will be updated by the proceedings of the Texas Conference held in September 1997. The book is organised into five sections, reflecting the mix of accepted papers and the predominant themes of the broader literature. As we approach the end of the 20th century we see a very strong intellectual support for the importance of research designed to improve our understanding of the behavioural responses of individuals in diverse contexts such as household decision making, organisation decisions and self-centred behavioural choice. The simplifications made historically about the formation of preferences and the stability of such preferences over time have been shown to be a primary candidate for error in predicting travel behaviour responses. While we cannot claim to have gained enough insights to totally reject historical predictions, we nevertheless are accumulating knowledge to enable researchers to distinguish between the necessity for further detail, because it does represent a significant contribution to im-

x


proved understanding of traveller behaviour, and where additional detail adds little insight. The challenge will always remain to pursue further insights about behaviour, but to be able to incrementally select new inputs for the state of practice. A very good example is stated preference methods, which have a history of 20 years in transportation, where the first 10 years might be best described as speculative and suspicious by most; however, a breakthrough came in the mid-eighties when it was recognised that such tools provide an appealing way of evaluating the role of new alternatives, which are sufficiently different from currently experienced alternatives. By the early nineties, the recognition of the power of information sourced from combining revealed and stated preference data placed stated preference methods firmly at the centre of state of the art studies of traveller behaviour. As we end the 20th century we see a strong promotion of stated preference methods as enrichment tools for a market-based approach to travel demand modelling. The book is organised into five parts. The first, Underpinnings of Travel Behaviour, contains both challenges and proposals. Garling criticises micro-economic theory as an adequate theoretical basis for travel choice modelling, Jara-Diaz proposes an expanded micro-economic framework defining travel decisions as part of activity assignment, Pendyala discusses causal analysis when multiple interrelated decisions are made, and Van Elslande and Dubois look at driver's information processing failures and their incidence on accidents. The article by Raux et al. tries to rescue the simulation of behaviour as a tool for travel analysis, given the willingness to deal with a road pricing experiment by individuals, in spite of their attitude against it. This is a good interface with the contents of the second part of the book. Six papers are included in the Stated Preference section. Two of them deal with questions and expectations. On the one hand, Bates shows the difficulties inherent in SP, not to dismiss the method but to be aware of them and to overcome problems cleverly. On the other hand, Widlert's claim of wide variability in valuation results from SP studies (the design affects the results) raises important questions about the design effects of SP experiments. Four interesting applications follow. Fridstrom and Madslien analyse freight choices for the Norwegian wholesale industry, Bradley deals with airport and air route choice, and Yen et al. study telecommuting behaviour. Finally, Matsumoto and Rojas use new techniques to generate stated preferences for their analysis of parking choices, with promising results.

Foreword

xi

The section on Travel Patterns contains two papers on trip matrix estimation from traffic counts, applied to different Scandinavian regions. Both contain methodological improvements (Abrahamson, and Nielsen), and Willumsen and Hounsell look at the effects of supply on highway travel time reliability. On mobility surveys, Kunert (using German data) shows the need to supplement information on individuals and households with information on the data collection process and Viegas and Gomes report and compare two large surveys undertaken in Portugal. On a different line, Pochet concludes that car ownership is the key variable to explain changes in travel patterns of elderly people in France. Sammer et al. predict from 7 to 9 percent share of trips for a new motorised bicycle in a small size city and the findings of Penn et al. suggest the possibility of using urban design parameters (such as grid configuration, height and width of streets) to arrive at better relations between pedestrians and vehicles in urban areas. The Dynamics of Route Choice is analysed from different perspectives in three papers. A dynamic switching model with state dependence and serial correlation is calibrated to represent departure time and route choice in Dallas, by Jou and Mahmassani. The same problem is studied by Nuzzolo and Russo, but in a different context with a different method; inter-city traffic assignment using penalty functions for deviations regarding target times. Lastly, van Berkum and van deer Mede present a successful application of a route choice model that includes information processing from a variable message sign. The concluding part on Methodological Advancements includes a collection of seven papers containing theoretical developments and applications. Three are related to discrete choice modelling theory (articles by Garrido, Morikawa and Sasaki, and Saleh and Bell). Tretvik and Wiedlert present a successful experiment on transferability of travel-to-work from Stockholm to Trondheim (one tenth of the population). Two deal with car use and duration modelling with extensions to competing risks (papers by Hensher and De Jong et al.), and one expands the scope of the book by looking at road issues (Lam et al.). We cannot conclude this presentation without mentioning that the book includes one of the last joint papers by the late Robert Herman, whose example as a true researcher and guide for many generations of students and colleagues, was with us during the meeting in Austin, Texas, where he lived and taught for many years to the benefit of our profession. The editors are indebted to Chris Pringle of Elsevier Science Ltd, Oxford, for his advice and encouragement in bringing this book to fruition,

xii


and encouraging the editors to deliver on time. We are also grateful to Stephen Bradley at Elsevier, Oxford, for his dedicated work as Language Editor and to Sabine Plantevin at Elsevier, Amsterdam, our Production Editor, who ensured that everything was done swiftly and efficiently. Finally, thanks are also due to Joe Finegan and his team at Scanway Graphics International, Dublin, for a job truly well done. Although the Chile conference took place three years prior to the final production of this book, we are confident that its contents contribute to the currency of the state of the art and practice in travel behaviour research. Juan de Dios Ortuzar David Hensher Sergio Jara-Diaz

Part I Underpinnings of Travel Behaviour

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1

Behavioural Assumptions Overlooked in Travel-Choice Modelling Tommy Garling

Abstract The substantial theoretical basis of travel-choice modelling is criticised for being an inaccurate description of how people make choices. Drawing on research in the behavioural sciences in general, and psychology in particular, several alternative behavioural assumptions are proposed. These include problem solving in connection with interdependent choices; information acquisition, representation, and use preceding choices; accuracy-effort trade-offs in the application of decision rules for making isolated choices; the constraining influence of social factors on selfish motives; and planning and automatisation in the implementation of choices. Although less simple and elegant than the current theory, such a set of more valid behavioural assumptions is in particular needed in a field concerned with applications.

Introduction In travel-choice modelling a clear distinction is not always made between the statistical theory, on the basis of which techniques for estimating model parameters are derived, and the substantial theory which must guide any modelling of a real-world process (such as making choices). It is unfortunate for the field that the former seems to have received much more attention than the latter. In this chapter, I will focus exclusively on the substantial theory. The point I am trying to make is that this theory overlooks several important behavioural assumptions. In reviewing the proceedings of two previous travel-behaviour conferences (Garling, 1993), I made the observation that behavioural assump-

4


tions are almost always made without reference to existing theories in the behavioural sciences. To some extent, I feel this is so because of ignorance. Another important reason is that behavioural-science theories are seldom quantitative. The first reason should be relatively easy to do something about. However, with regard to the second reason, travel-choice modellers may need to realise that quantitative behavioural theories may be unattainable. At least this is the point Simon (1990) makes in a major paper. He argues that we may be unjustified in believing that it will ever be possible to discover quantitative laws that apply to human behaviour. If this statement is taken seriously, quantitative forecasting of travel demand, for instance, may never be feasible. Therefore, new ways of approaching forecasting problems are called for. Following my criticism in the next section, I will attempt to provide alternative behavioural assumptions based on current research in the behavioural sciences (in particular psychology). These assumptions have guided my own work in the field (e.g., Garling et al., 1989, 1997a, 1997b; Golledge et al., 1994). However, a full-fledged alternative theory does not exist. My suggestions are meant to indicate directions I believe future developments need to take.

Criticism of the Theoretical Basis of Travel Choice Modelling Current travel-choice modelling is based on microeconomic theory (BenAkiva and Lerman, 1985). However, for more than 30 years behavioural scientists (e.g., Camerer, 1989; Edwards, 1954; Simon, 1982; Kahneman and Tversky, 1979; see Abelson and Levy, 1985, for a review) have been arguing, and occasionally economists themselves have been arguing (March, 1978; Thaler, 1992), that this theory is not an accurate description of how people make decisions.a Contrary to a basic assumption of the theory, people's preferences have been shown to be inconsistent. For instance, there is evidence that preferences are intransitive (e.g., Tversky, 1969), that they change over time (e.g., Loewenstein and Prelec, 1993; a

Implied by this sentence is that one, or more, deliberate decisions are first made to choose something, then the choice is (observed to be) executed. Subsequently, the term decision is used interchangeably with choice. It should be clear from the context when it is assumed that choice (or behaviour) is not preceded by decisions.

Behavioural Assumptions Overlooked in Travel-Choice Modelling

5

Stevenson, 1986), and that they are influenced by the elicitation procedure (e.g., Fischhoff, 1991). I am aware of two counter arguments. One is that people's preferences are only inconsistent if they are stated, and not if they are revealed in "real behaviour". It is true that personal involvement, requirement to justify a choice to others, and real consequences of decisions occasionally have been shown to affect choices empirically (Payne et al., 1992). However, it is not always the case that such effects can be interpreted as support for microeconomic theory (Grether and Plott, 1979; Slovic and Lichtenstein, 1983). In fact, inconsistencies may sometimes increase rather than decrease. The other counter argument, formalised in random utility theory (BenAkiva and Lerman, 1985), is that inconsistencies ("taste variation") cancel at an aggregate level. Unfortunately, techniques for estimating parameters in models (such as the logit) do not seem to provide sensitive tests of systematic deviations. In fact, as shown by Camerer (1987, 1992), in markets many known systematic biases people are susceptible to, demonstrated for years in psychological research (e.g., Kahneman et al., 1982), do not cancel. On the contrary, they may contribute significantly to market dysfunction. Microeconomic theory is also incomplete. Neither does it specify what utility is (apart from a hypothetical variable lacking any other theoretical meaning), nor how it is maximised by a decision maker. The first incompleteness leads to the following circularity (McNulty, 1990): A person chooses an alternative A over B because he or she prefers it; A is preferred over B because the person chooses it. The vagueness of the concept of utility was recently noted by Kahneman and Snell (1990) who argue that a distinction should be made between experienced utility (satisfaction from consuming a good) predicted utility (anticipating satisfaction from consuming a good), and decision utility (the weight assigned to the outcome consuming the good when making a decision). In a similar vein, Garling et al. (1996) suggested a classification of utility with reference to a temporal and an experience dimension. In other research, carried out in collaboration with different colleagues (Lindberg et al., 1989, 1992), we have successfully, in some cases, attempted to find a relation between utility maximised in choices (for example of residential location) and psychologically meaningful motivational concepts such as, "happiness", "an interesting life", "inner harmony", and "moral obligation". Another related target of criticism is the underlying assumption that utility refers

6


to the selfishness motive. As discussed in Biel and Garling (1995) and, related to travel behaviour, in Garling and Sandberg (1997), research in social psychology has documented that social motives may sometimes be as equally important as selfishness for people's choices. There is also a second form of incompleteness. At best microeconomic theory specifies the variables affecting choices, tacitly assuming that information about these variables are available to the decision maker. Such assumptions are, however, frequently unjustified. They may be relaxed if it can be assumed instead that people are simply not completely informed, not that they are systematically misinformed. However, underlying the notion of bounded rationality (Simon, 1982), errors people make in acquiring and processing information before making choices are often systematic. Such findings suggest that alternative theories are both needed and feasible. Some years ago, my colleagues and I published a conceptual paper (Garling et al., 1984) where we argued that travel is guided by plans. As noted by Walmsley (1988), this is neither a new insight, nor is it hardly more than a trivial statement. However, our main point was not so much that travel is guided by plans but that information acquisition is. Our goal was to understand why people acquire cognitive maps of their environments with the properties they appear to have. Somewhat naively, we thought at that time that travel behaviour researchers would be interested in our ideas. However, with few exceptions (see, Axhausen and Garling, 1992) conceptualisations of travel choices as plans do not exist. There are also few, if any, accurate conceptualisations of information acquisition, representation and use. Once, I even heard the argument that cognitively represented information plays no role, because "objective" information about destinations is a better predictor of choices of destinations than peoples responses to questions aimed at measuring knowledge of destinations. This finding is, of course, not surprising, if reliable measures are lacking cognitive information. On what other information than a cognitive map could a choice of (for instance, a destination) be based? As we discussed (Garling et al., 1984), there are external sources of information (e.g., actual maps) that people may access. Choices are sometimes also made when people are in the place, in which case they can rely on perceptual information. A third possibility is that people are able to make educated guesses on the basis of other knowledge which they have. Yet, it cannot be denied that people acquire information which they later retrieve and use. Research shows that people acquire cognitive maps witness this (Garling and Golledge,


7

1989). Why then should people not use the information they acquire? If it is used, inaccuracies in this information will, however, affect choices. Such systematic inaccuracies are likely to apply to most people. An example is the systematic errors people seem to make in judging spatial relations (e.g., Tversky, 1981). Another example is the many systematic errors people make in forecasting events (e.g., Kahneman et al., 1982). Still another issue concerns the implementation of a choice. Since microeconomic theory does not specify the process preceding an observed choice (behaviour), this important problem has been overlooked. A few economists (Hoch and Loewenstein, 1991; Thaler and Shefrin, 1981), have been more insightful. They discussed the use of self-control techniques in the implementation of choices. Repeated successful implementation often entails developing habits. Theorising about how habits are acquired has a long tradition in behavioural research (e.g., Garling and Garvill, 1993; Roniset al., 1989). In summary, microeconomic theory is both an invalid and incomplete description of how people make choices. Therefore, it is not an appropriate theoretical basis of travel-choice modelling. In particular, the theory fails to account for (1) that choices are often part of plans; (2) that people show systematic biases in acquiring, representing and using information on which choices are based; (3) that choices, or preferences, are inconsistent; (4) that the concept of utility refers to many different entities, not all of which are related to a selfish motive; and (5) that choices are implemented through a process which sometimes entails developing habits.

Some Suggestions of Alternative Behavioural Assumptions Interdependent decisions It has been recognised that choices are frequently interdependent. In such cases, the interdependent choices are simply modelled by expanding the choice set to include all possible combinations of options. However, interdependencies go further than this. Like many other activities, making a trip requires the formation of a plan (Garling et al., 1984; Miller et al., 1960). Formation of a plan entails problem solving as it has been studied in psychological laboratories: Alternatives are generated after heuristic search in a solution space, evaluated according to designated criteria, selected and implemented. Starting with the work of Newell and Simon

8


(1972), production systems have proved to be a useful means of modelling problem solving. A production system is a set of instructions specifying the conditions under which actions should be undertaken. A review of production-system models, relevant to travel-choice modelling, is found in Garling et al. (1994). Current research on human problem solving is reviewed in many sources (e.g., Lesgold, 1988). An example of what may be called a problem-solving theory of decision making is given in Hayes-Roth and Hayes-Roth (1979). In their production-system model of how people "plan a day's errands" implemented in a computer program, planning is not a linear sequence of decisions. Rather than proceeding hierarchically from a global schematic plan, to a more refined plan, people are modelled as opportunistic in their planning. For instance, tentative decisions to perform some initial activities may highlight constraints on the planning of later activities and cause a refocusing on their planning. Furthermore, a person may decide that there is insufficient time to plan ahead, do only some rudimentary planning, or to plan meticulously. Such meta-decisions of how much to plan are also integral parts of the planning process. Other meta-decisions concern the criteria to evaluate the plan, what types of decisions to make and by what heuristics to make the decisions. In planning people change forth and back between the different levels of abstraction, rather than always proceeding orderly from the more to the less abstract. In addition to outlining the properties of planning, Hayes-Roth and Hayes-Roth (1979) attempted to specify underlying mechanisms of problem solving. This was done on the basis of data collected from subjects by means of think-aloud protocols (Ericson and Simon, 1984), as well as other conventional techniques in psychological experimentation, such as chronometry and error analyses (Ericson and Oliver, 1988). The assumption is made that planning comprises the independent actions of many "cognitive specialists", who record their decisions in a common data structure. On the basis of this available information, each specialist makes tentative decisions to be incorporated into the plan. These decisions concern the plan itself, what data are useful in developing the plan, desirable attributes of plan decisions and how to approach the planning problem. Some of the specialists suggest high-level, abstract additions to the plan, while others suggest detailed sequences of specific operations. An executive makes decisions about how to allocate cognitive resources, what types of decisions to make at certain points in time, and how to resolve conflicts when there are competing decisions. My example has two implications for the current practice of travel-


9

choice modelling. (1) The interdependence of travel choices is more conditional on the external circumstances than is usually believed; and (2) how interdependent travel decisions are made depends to a larger extent on meta-decisions than is usually believed.

Information acquisition, representation and use It is apparent that acquisition, representation and use of information, play an important role for travel choices. Unknown alternatives are not chosen. If the consequences of choosing an alternative are unknown, or misrepresented, they will also affect the choice. Thus, it is obvious that risk and uncertainty are invariably associated with consequences, although almost never considered in travel-choice modelling. A first step is to specify what information is relevant. Such a specification must take as its starting point an analysis of the information possessed by the decision maker rather than what information ought to be relevant. In particular environments, people know how far away destinations are, how they can get there, how places look so that they can be recognised from different angles, and how useful different places are in relation to the current purpose (Garling and Golledge, 1989). However, such information is never complete. Usually it is also in some ways systematically distorted (e.g., Tversky, 1981). There are several attempts at production-system modelling of the acquisition, representation and use of information about the environment (e.g., Gopal et al., 1989). The availability of geographical information systems may make further such efforts feasible (Golledge et al., 1994). Several questions remain to be answered, such as how distortions are produced, the role of affective components in the process, and how decisions depend on how the information is acquired and represented. With respect to the last question it has been shown, for instance, that a map-like spatial representation is more easily acquired if people have access to a map (Thorndyke and Hayes-Roth, 1982). In a series of studies (e.g., Garling, 1989; Garling and Garling, 1988), my collaborators and I showed that whether, or not, a spatial representation is cognitively available affects choices. It is never the case that travellers choosing among alternatives are informed about probabilities of the outcomes. However, this does not mean that they assume that the outcomes are certain. Instead, the travellers are likely to supply their own probabilities in evaluating the different alternatives. Choices will be affected since the judgements of probability

10


are probably used to weight the consequences (Tversky and Kahneman, 1992). How judgements of uncertainty are made have been extensively studied. In general they are based on heuristics which sometimes are biased relative to base rates (Kahneman et al., 1982). For instance, if a fatal accident becomes known through the mass media, many people will judge the probability of an accident to be higher. Another bias is a tendency to be overoptimistic (Zakay, 1983), at least about one's own future (Sjoberg and Biel, 1983). In making choices people who do not know the objective probability of consequences may distort them in a way consistent with such overoptimism (Hogarth and Einhorn, 1990). My examples in this subsection suggest, (1) that choice sets should be modelled more accurately with regard to how people actually acquire and represent information about environments; and (2) that the influence of risk and uncertainty on choices cannot be overlooked.

Decision rules In a major behavioural theory of how people make single, isolated choices among multiattribute alternatives, Payne et al. (1993) proposed a constructive view of decision making which captures much of the accumulated knowledge of contemporary research in the area. According to this theory, people use different heuristic decision rules in adjusting to demands such as time pressure, information overload and accuracy standards. The theory may be seen as a special case of the model proposed by Hayes-Roth and Hayes-Roth (1979), although Payne et al. (1993) argue that "top-down" processes play a more decisive role than Hayes-Roth and Hayes-Roth conjectured. Yet, instead of using the expected utility/value, additive utility or weighted additive utility decision rules, the cornerstones of utility maximisation, people use many other decision rules in response to task demands. The same single choice may even be preceded by the sequential application of several decision rules (Tversky, 1972). For instance, the number of alternatives may first be screened by means of an eliminationby-aspects, disjunctive or conjunctive decision rule, then a weighted additive decision rule used to select one of the remaining alternatives. On the basis of a computer simulation, Payne et al. (1993) were able to define how much effort each of several decision rules required. For instance, the lexicographic decision rule (choosing on the basis of the most important attribute differentiating between the alternatives) was found to require much less effort than the weighted additive decision rule (choosing


11

the alternative with the highest weighted sum of utility across all attributes). It was also found that the former rule often picked the same alternative as the latter. When introducing time pressure, the lexicographic rule actually picked this alternative much more frequently. Empirically it was shown that people tended to achieve an optimal accuracy-effort tradeoff. Another reason why people prefer decision rules other than additive utility is that these alternative rules impose less requirements on tradeoffs which presuppose an interval-scale representation of utility (Svenson, 1979). If an alternative can be found that dominates all other alternatives (i.e., an alternative that is better on at least one attribute and not worse on any), a trade-off is unnecessary. In this vein, it has been suggested that decision makers attempt to find alternatives which dominates others (Montgomery, 1989). The lexicographic decision rule is an example of that. Its application requires that people are willing to place more weight on one attribute. Another example is the conjunctive decision rule reflecting the satisficing principle (Simon, 1982): Alternatives are processed sequentially and the first one fulfilling specified criteria is chosen. As a consequence, trade-offs representing conflicts are avoided. The inconsistencies of preferences which microeconomic theory has difficulty in accommodating simply reflect the use of heuristic decision rules. Several empirically supported rationales for using such decision rules have been given. It would certainly be a mistake to conclude that observations that people use heuristic decision rules are inaccurate. It is not the observations that are in error but the theory that fails to predict them.

Selfish versus social motives In economics, selfishness is assumed to explain much individual behaviour (Samuelson, 1983). However, the picture is less simple. When there is a conflict between self-interest and what is good for the society at large, people do not always act in self-interest. In research on social dilemmas (Caporael et al., 1989; Liebrand et al., 1992) where the outcome of the self-interest choice becomes the worse alternative if a majority makes this choice, it has been found that some people restrain themselves and cooperate, even though they are anonymous and do not know how others choose. These people are assumed to have a pro-social value orientation (Liebrand and McClintock, 1988). In contrast, a pro-self value orientation

12


predisposes people to act in self-interest. This, however, may be constrained. Social factors (communication, personal responsibility, group identification) are known to constrain selfishness. The dilemma structure (payoff, information feedback) is also known to do this. Biel and Garling (1995) assumed that differences depending on social value orientation remain even though self-interest is constrained. Social dilemmas have individual and collective consequences. For instance, positive consequences of choosing the automobile may be travel time, flexibility and comfort. These are consequences of a single-trip experienced by each individual. Negative consequences are noise, congestion, air pollution, energy depletion and traffic accidents. These collective consequences depend on the number of people who make the choice. They also have consequences for the individuals who in varying degrees, are exposed to noise, congestion, etc. When making the choice people may consider the individual consequences, collective consequences or individual outcomes of the collective consequences. We assume that the collective consequences are always salient to pro-social individuals. Furthermore, the individuals ignore the uncertainty associated with such consequences. To pro-self persons, the individual consequences are salient. If their selfinterest is constrained, they still focus on the individual outcomes of the collective consequences. Furthermore, they are more strongly affected by uncertainty. There are reasons to question that selfish motives always underlie choices people make. Even if they do, other motives may be prevalent if the social situation constrains self-interest. An alternative theory must accommodate such observations.

Implementing choices A decision is only an intention or commitment to behave. Reflecting that preferences may be inconsistent over time, the decision maker sometimes changes his mind and chooses not to carry out the behaviour. Under what circumstances does this occur? In other words, when are people's behaviour possible to predict from their stated choices? The latter issue has been an important topic in attitude research (Dawes and Smith, 1985). If the behaviour is carefully planned, it is more likely to be carried out due to a higher degree of commitment. Alternative behaviours may also appear less attractive than they would otherwise do (Svenson, 1992). In the case where an individual exerts control, careful planning should be


13

furthermore effective in preventing obstacles from interfering with a chosen action. The determinants of initiating a behaviour are frequently not the same as those factors which determine persistence (Ronis et al., 1989). A frequently repeated behaviour (such as commuting by automobile) is not necessarily preceded by deliberate decisions. Such behaviours are performed automatically. Several theories of automatisation have been proposed (e.g., Anderson 1982). One important consequence of automatisation is that the behaviour may be inconsistent with attitudes (Chaiken and Yates, 1985). Breaking a habit, which is not preferred, presupposes that there are alternatives which people become aware of, that the alternatives look better, the alternatives are not forgotten and the alternatives are eventually experienced as better. Implementing a choice is an important neglected phase. Time-inconsistent preferences are one reason why choices are sometimes not implemented. Automatisation explains why, in other cases, nonpreferred behaviours are performed.

Conclusions Taking my point of departure, a critical assessment of microeconomic theory (Ben-Akiva and Lerman, 1985) for being an inappropriate substantial theory for travel-choice modelling, I suggested several psychological theories that entail behavioural assumptions which are more plausible. Unfortunately, far from anything as simple and elegant as microeconomic theory has been possible to suggest. I think it must be realised that, at least at this time, there is an unavoidable trade-off between simplicity and elegance on the one hand, and accuracy on the other. In travel behaviour research, with its strong emphasis on applications, the latter should clearly be preferable if the contributions of the research are judged with regard to their relevance to real-world problems.

Acknowledgements The ideas discussed in this paper were developed in connection with a research project which was financially supported by grant No. 91-238-63 from the Swedish Transportation and Communications Research Board.

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2

A General Micromodel of Users' Behaviour: Basic Issues Sergio R. Jam-Diaz

Abstract From a microeconomic viewpoint, the so-called modal utility in discrete choice models is a conditional indirect utility function, which represents the maximum level of satisfaction that can be reached at given prices and income if a particular mode was chosen. Therefore, its functional form represents implicitly, or explicitly, the analytical solution of an optimisation problem, thus, both the specification of direct utility (variables and form) and the type of constraints considered determine the specification of modal utility. In this chapter, the main issues behind the formulation of a general microeconomic model of users' behaviour are discussed. The model is motivated by, and contrasted against, other forms of representing the primal problem of individual behaviour, coming either from the microeconomic literature on the role of time in consumer's behaviour (Becker, 1965; DeSerpa, 1971; Evans, 1972), or from the approaches that yield, or discuss mode choice models with a microeconomic perspective (McFadden, 1981; Train and McFadden, 1978; Viton, 1985; Truong and Hensher, 1985; Bates, 1987; Jara-Diaz and Farah, 1987). The implications of the new formulation on actual modelling under different assumptions are presented and discussed.

Introduction In discrete mode choice modelling, utility is, in fact, a truncated conditional indirect utility function. Therefore, it corresponds to the solution of an optimisation problem representing consumer behaviour. The functional form, and the arguments of modal utility, are thus determined by the analytical properties of direct utility, by the variables that are assumed to influence this latter and by the type of constraints considered.

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The aim of this chapter is to detect and discuss the issues behind the formulation of a model for consumer behaviour, in order to justify a new framework to understand users' choices. In the next section, I review the foundations of consumer models that include time in the analysis; in the third section, the microeconomics of mode choice are critically synthesised. Then I formulate a model for users' behaviour, discussing the issues raised in the previous analysis. Additional comments are given in the final section.

Transport Related Microeconomic Individual Behaviour Models The introduction of time in the models of individual behaviour was a major step towards the understanding of transport within the context of human activities. These microeconomic models have had a great influence in the subsequent generation of theoretical approaches that have provided the foundations for the presently used specifications of (discrete) mode choice models. The particular form in which each author formulates the basic problem of utility maximisation subject to different type of constraints, has an impact on the interpretation of behaviour. And there are important differences among the most relevant of the articles in this area of literature. It is well known that the basic consumer model postulates that utility depends upon the level of consumption of market goods (X), which is maximised subject to a budget constraint. In his pioneering article, Becker (1965) presents a formulation in which utility depends on the consumption of "basic commodities" (Z), which require both market goods and time to be prepared (T); he then introduces income and time constraints, including working hours as a variable (W}. So his basic model is

subject to

where r is total time available, IF is other (fixed) income and w is the wage rate. As Becker (1965, pp. 496-497) points out, the constraints are

A General Micromodel of Users' Behaviour

21

not independent "because time can be converted into goods by using less time at consumption and more at work". In fact, if one solves for W in equation (3) and replaces the result in equation (2), then

where bt and tt are the market goods and time requirement per unit of Z,. Becker names the righthand side of equation (4) "full income", and represents the maximum amount of money the individual could make working the whole period. Under this setting, the term that multiplies Z/ represents the full price of consumption, including the expenditure on the necessary market goods plus foregone income. The main contribution of Becker is to analyse all properties of demand in terms of this "full price", that has a time component. It is fairly evident that Becker's assertion of "time can be converted into goods" is possible because of two apparently innocent features of the model: first, working hours can be chosen freely, and second, they do not appear as arguments in the utility function. This, indeed, has an impact in terms of the interpretation of the results. Besides some other problems in the model, these are important limitations which are taken into account in subsequent influential pieces by DeSerpa (1971) and Evans (1972). However, Becker's work remain an important reference.3 A few years later, DeSerpa (1971) proposed a model in which the time necessary to consume market good i affects utility, together with the amount consumed. He includes both income and time constraints, and added what can be identified as the first set of "technical" constraints, in the form of minimum time requirements to consume a given market good. Thus, his model looks like

subject to

22


where T now includes all activities. Note that, although utility has the same arguments as Becker's model, this is justified in a different direction, as the time devoted to consumption has a direct effect on the individual's level of satisfaction. Within this context, it is important to note that DeSerpa calls the consumption of Xt an "activity". Also, he explains that the model can be generalised to include a work commodity, "pure time" commodities and negative prices. He is the first to point out that the value of saving time in the activity is positive only when the corresponding time consumption constraint is active (i.e. the individual would have liked to spend less time than required). DeSerpa's framework helps to clarify important conceptual aspects regarding the value of time, and his article is a valuable source for further discussion. Key points and views regarding utility and constraints were neatly exposed by Evans (1972). He made a strong remark regarding Becker's synthesis of constraints (2) and (3), recalling that working time should also be included as an argument in utility (previously mentioned by Johnson, 1966; Oort, 1969), which would prevent W from being used as a "pivot" variable in the constraints. After this, Evans proposes a model in which the only source of direct utility is the time devoted to the different activities; market goods enter the picture as inputs that are necessary to develop each activity, and they are the source of the activity cost. Pure time activities are allowed to exist simply as a particular case, and their cost can be either positive (the individuals pays), or negative (the individual is paid). Besides, Evans introduces a set of constraints representing relations among activities, which means that time devoted to activity / can be technically related with time spent on activity;. Formally (in our notation) Evans model is

subject to


23

where Q is a matrix containing the input of goods at a certain rate per unit of time, which are required for each activity, and B is a matrix that links activity times. As T includes all activities, equation (10) is the budget constraint with a negative wage rate as one component of P. If the three basic articles are viewed from a positive perspective, we can see similarities and differences. All three are extensions of the basic consumer approach to encompass time requirements for consumption, which induces the need for a time availability constraint. The arguments in (direct) utility are the basic commodities Z (Becker, 1965), goods X and consumption time T (DeSerpa, 1971), and activity time T (Evans, 1972). Although these three views of utility might appear as an issue to be discussed regardless of the role of the constraints, it is more rewarding to look at the whole formulation in each case. In my view, one of the most important aspects in these models that include goods and time, is precisely the relation between them; and this is only present in Evans' framework. This is evident when one sees that, in order to get equation (10), the relation

has to hold. Equation (14) is an explicit transformation function with fixed coefficients, which gives the combination of outputs needed to perform a set T of activities. This does not appear in DeSerpa's framework, because TI is directly linked to Xt. In my view, the physical link between goods and time is a key aspect in the formulation of a general model of consumer behaviour. Before discussing the approaches that are specifically designed to understand transportation decisions (mode choice, in fact), let me point out three aspects that are very much related to these important articles. First, note that the very existence of a transformation function between goods and time would make a U(X, T) type utility function a U(T) one, but that this would not be neutral regarding the budget constraint, because X is postulated as a direct source of utility. Second, only Evans introduces an explicit set of relations among activities (time) which goes beyond the fact that similar (or the same) goods can be involved in two different activities; one can claim, however, that if two activities are closely related functionally, then a single (aggregate) activity could always be defined. Finally, note that commodity consumption is still the central point in both Becker's and DeSerpa's frameworks; as for Becker, T is a factor to produce final goods Z, and for DeSerpa T is part of the description of X;

24


in Evans, however, it is X that plays a "secondary" role, as buying goods is a means to enjoy T, and this is conceptually neat and reasonable.

Microeconomic Foundations of Discrete Mode Choice Models The literature on the microeconomics of mode choice is not as numerous as one could have imagined. And, still today, the most influential pieces are the Train and McFadden (1978) goods-leisure trade off framework, and McFadden's (1981) ambitious piece. For the purposes of this chapter, the article by Truong and Hensher (1985), Bates' (1987) sharp comment, and the interpretative synthesis by Bates and Roberts (1986), are valuable sources of discussion, although all three were meant to be contributions to the discussion on the value of time. For different reasons, the articles by Viton (1985) and Jara-Diaz and Farah (1987) have some relevance in our discussion because of the analysis of the role of income. The goods-leisure trade off framework for mode choice models (Train and McFadden, 1978) postulates that the basic sources of utility are the consumption of goods and the time devoted to leisure. In the original formulation, the conflict between both sources is due to the choice of working hours, paid at a fixed (exogenous) rate: the more the individual works, the more s/he can consume and the less leisure time is available. The two constraints are income and time related, respectively; in both, working hours is the key adjustable variable, and mode choice appears as a (discrete) decision among feasible pairs (c/, ?/), where ct and r, are travel cost and time (for mode /), respectively. Letting working hours play the central role in the constraints makes this approach a Becker-type one, as the opportunity cost of any activity (leisure or travel) will always be the wage rate. And this happens in spite of the seemingly different utility formulations. In our restatement of the goods-leisure framework (Jara-Diaz and Farah, 1987), we imposed an exogenous working schedule to the individual (we have called this the "public servant" framework), such that both income and working time are fixed and given. Under this setting, mode choice (i.e. the choice of ct and tf from a discrete set) is the only source of conflict between consumption and leisure, induced by the existence of fast-expensive modes and slow-cheap ones. We have shown that, in this case, what matters is the time available to spend the given income, which originates our expenditure rate models (Jara-Diaz and Ortuzar, 1989).


25

A general formulation of the goods-leisure approach can be synthesised as follows:

subject to

where G is goods expenditure, and L leisure time (v and /implies variable and fixed, respectively). The variable working time Wv is now a choice. If this is compared against Becker's equations (1), (2) and (3), G is 2) PfXi, Wv + WF = W, and L = S Tt. Note, that again working time does not appear in the direct utility function. Replacing both G and L in U by the corresponding functions of Wv from the constraints, problem (15)(18) turns into an unconstrained one which can be written as

mode choice set. The solution of problem (19) for i e M,

WV^Q

where M is the mode choice set. The solution of equation (19) for a given pair (c(, tf) yields a conditional optimum for Wv, which is, in fact, W*(IF- c{, T - WF— ?,-). Replacing this function in the original utility expression, a conditional indirect utility function is obtained. This analytical procedure works well if we end up with a positive value for Wv. However, if the case is one in which the individual is working more than desired (i.e. s/he would reduce WF if possible), then Wv will be zero and the only source of trade-off between goods and leisure is mode choice; equations (15)-(18) encompasses both cases. If Wv happens to be positive (which is something truly observable), then the value of leisure is the wage rate under this framework. Therefore, although they look different and their utilities have different foundations, Train and McFadden (1978) and Becker (1965) are in fact the same model. Let me recall, however, that X and T in Becker are the

26


inputs to obtain the basic commodities Z, and they are treated as a vector, as opposed to G and L, which are aggregates. The framework proposed by Truong and Hensher (1985), cleverly corrected and enhanced by Bates (1987), is useful to highlight a couple of points that are very important for the purpose of this chapter. Truong, Hensher and Bates (THE) tried to translate both Becker's and DeSerpa's general frameworks into (discrete) mode choice formulations. Due to the presence of DeSerpa's technical constraints regarding minimum time requirements (see equation (8)), they show that the conditional indirect (modal) utility should have a mode-specific time coefficient; this coefficient should be generic if mode choice was derived from Becker's framework. This difference is also influenced by the fact that travel time does not enter direct utility in the so-called Becker type model, while it does not appear explicitly in DeSerpa's counterpart. In both cases, the THE formulation follows the goods-leisure approach which, as we have seen, is in fact Becker's. However, since goods and "activities" in DeSerpa's are also vectors explicitly written as such, also since working time is not adjustable, and since additional time constraints appear, interpreting DeSerpa's utility arguments X and T as goods and leisure is a misuse. Thus, what to use as an argument in direct utility, what constraints should be considered, and what is fixed or what is variable, are key decisions in proposing a framework for the modelling and understanding of travel decisions. As Viton (1985) pointed out, the modeller should also be careful with the way income is treated in modal utility, a point that later we have hopefully contributed to solve more precisely as part of an approach to detect income effect in mode choice (Jara-Diaz and Videla, 1989).

A General Model for Travel Behaviour As we have seen, there are basic questions that must be answered in order to formulate a model for travellers. From the literature, each one can be formulated as a particular problem and given a general interpretation. First, an explicitly relevant one: should working time enter direct utility as an argument? (both Evans and Bates highlight the importance of this). My position coincides with Evans: that the basic source of utility is the time devoted to the different activities, to all activities, including work, travel, sleep, chat and so on. In fact, this view has received some attention within the last decade, as in Winston (1987), who emphasises activity


27

scheduling, and luster (1990), who sees "process benefits from time uses" as the main source of satisfaction (surprisingly, neither mention Evans paper). And then comes the second question: how do goods enter the picture? In a twofold manner, I propose: as necessary items to perform the different activities, and as the basic source of expenses. Third, should we account for time constraints on each activity? In my view, the time period devoted to each activity is potentially related to all other activities in two ways: direct time dependency (i.e. the duration of one activity affecting the duration of other activities), and through the use of common (or interrelated) goods. According to the preceding discussion on the relevant approaches and theories, a model of travel choices can be looked at as a time allocation problem, recognising that utility is directly derived from what the individual does (activities), which requires goods that are costly. Therefore, I propose the following model:

T

s

u

b

j

e

c

t

t

o

where W/ is working time in period T (i = F, fixed; i = v, variable); T is a vector of activity times Tt in period T; r is a vector of travel times ?,, in period r; B is the number of trips in period r; 8r; = 1 if mode / is used in trip / (0 otherwise); F is a technical transformation function that converts T into X and vice versa; Xid is the amount of good / bought in zone d in period r; Pid is the price of good / in zone d; MJ is the set of modes available for trip /. Thus, all activities have a direct impact on utility, in spite of the fact that some of them are sought out (pleasurable activities) and some are required or unavoidable, but ideally would not be performed. The sum of all activity times includes work, travel and pure idle time in addition

28


to sleeping, eating, talking, shopping, watching TV and so on. But, as justified earlier, activities are interrelated by themselves and through goods requirement. Therefore, one cannot view equations (20) and (21) as an independent optimisation subproblem, because it requires the set of relations represented by the transformation function (22), which in turn, makes the optimal solution dependent on goods consumption. In this model, goods can be bought in different locations and, at potentially different prices. As residence and work places are given, the number of trips is only sensitive to the choice of X, a relation which appears as equation (24); this can be viewed as the result of a network related subproblem (e.g. optimal number of trips given X). Under this view, the known (given) variables are WF, IF, ?,;-> c,7, r, Pid and w, while the decision variables are {Tf}, {§,7}, {Xid}, Wv and B. The solution for B is the generation model, the solution for X is the distribution model and the solution for d is the mode choice model. This formulation is not compatible with the goods-leisure framework, though. As discussed earlier, ^Tt = L and *2PidXid=G; because of the technical relation between X and T, there is an implicit relation between G and L, which has a straightforward interpretation: goods consumption requires L and vice versa, which is a missing fact in both the Becker and Train-McFadden models. In order to explore some implications of this model, let us see how it develops when analysing mode choice in the case of one trip k. This is a useful exercise, as it is the prevailing modelling practice in the field. All other trip decisions will be assumed as given, i.e. number of trips B, destinations (which are one of the dimensions in X), and all other mode choices. Then we can write

subject to

plus the non-negativity constraints. For simplicity only, relation (24) be-


29

tween B and X has been dropped, which means that the amount of goods does not affect the number of trips. As usual in the discrete choice approach, problem (25)-(28) can be solved conditional on mode choice, which yields conditional solutions for T, T±X and W. Formally,

where t and c are obviously defined, and tp is the vector of travel times except tik. Then the conditional indirect utility function corresponds to

In spite of its generality, equation (32) is very helpful in explicitly showing some key aspects in the specification of modal utility. First, unlike modal cost, travel time plays a dual role in the indirect utility: it provides direct (dis)satisfaction, as a survivor from U in equation (25), and it affects available time to do other activities, as a consequence of constraint (26). This second role of travel time deals with the trade-off with pleasurable activities, e.g. sleeping until late or playing guitar with your children at night (or working more). The thing is, and this is part of the second key aspect, that both roles can not be distinguished if Vin (32) is approximated linearly; if this was accepted as a reasonable representation of (indirect) utility, the conditional comparisons would be based upon an expression like:

and the only terms that would influence mode choice would be

30


from which one can only estimate (g-a) and d, but neither g nor a can be obtained. Note also that a first-order approximation like equation (33) would make all variables except travel time and cost, irrelevant (e.g. no income effect). This would not happen if a second-order expansion was considered a better model than this. In fact, our test for the detection of income effect in mode choice rests ultimately on the significance of a second order term (Jara-Diaz and Videla, 1989); a similar approach is used to theoretically justify a segmentation by distance travelled (JaraDiaz, 1990). The third point that I would like to highlight is the role of the wage rate w. In this framework, the relevant value of w is the hourly payment the individual is offered to do extra work; it is true that this might have a relation with IF/WFbut, under the "public servant" scheme, w represents the real opportunity cost of activities performed outside the (fixed) working schedule. According to this, individuals in a sample should be asked about their work arrangement; if the individual has a fixed salary and fixed working time, s/he should be asked the value of the wage rate for additional work, as this is the value that should enter modal utility. Unlike direct utility, the conditional indirect modal utility in equation (32) can be interpreted in terms of "goods and leisure", as the first argument is, in fact, the total time available to perform T (which I have associated to L), or to keep on working, and the last argument is the time equivalent to buy X, i.e. G/w, minus the actual extra time worked. Formally,

which explicitly shows the difference with the U(G, L) approach.

Final Comments From a critical but constructive analysis of the microeconomic foundations of models related to trip decisions, some issues have been clearly established. First is the question of the sources of direct utility; starting from goods consumed and going through the concept of basic commodities, consumption time appeared as a necessary item to realise utility. After this shy beginning, time devoted to activities emerged as the basic source


31

of satisfaction, and it is the goods that should be seen as the means to an end. Once this is accepted, every single minute in a period should be considered. The preceding analysis makes both work and travel times, variables that enter utility with the same rights and duties as all other activities. Thus, time can not be converted into money (through more work) without altering utility, which makes the fusion of income and time constraints a mistake. Second, the traditional time and income budget constraints are not enough to complete the picture of individual behaviour, as market goods and consumption time are related (as well as activities themselves). Just as the previous point, this also weakens the conceptual foundations of the goods-leisure framework. The addition of a set of technical constraints induces the need to perform certain activities which would be omitted otherwise. This is a point raised originally by DeSerpa and Evans, introduced later in the discrete choice literature by Hensher and Bates. It is surprising, though, that no explicit reference to a transformation function has been made within the context of mode choice, or that goods and leisure are still kept as the basic sources of utility. In my view, this needs revision and discussions; Evans contribution seems to be the best departure point. I hope this chapter will encourage further work in this direction. Finally, in agreement with Juster (1990), let me add that the vector of goods, X, should be rescued as a direct source of utility in addition to the time devoted to each activity. The main reason is that one should expect the marginal utility derived from a given activity to be dependent on the type and amount of goods involved. Thus, a U(X, T) utility function might well be justifiable through an activity approach, but emphasis would be on T rather than on X.

Acknowledgement This research was partially funded by FONDECYT, Chile.

References Bates, J.J. (1987) Measuring travel time values with a discrete choice model: a note. The Economic Journal 97, 493-498.

32


Bates, J.J. and Roberts, M. (1986) Value of time research: summary of methodology and findings. Proceedings 14th PTRC Summer Annual Meeting, University of Sussex, July 1986, UK. Becker, G. (1965) A theory of the allocation of time. The Economic Journal 75, 493-517. DeSerpa, A. (1971) A theory of the economics of time. The Economic Journal 81, 828-846. Evans, A. (1972) On the theory of the valuation and allocation of time. Scottish Journal of Political Economy 19, 1-17. Gaudry, M.J.I., Jara-Diaz, S.R. and Ortuzar, J. de D. (1989) Value of time sensitivity to model specification. Transportation Research 23B, 151-158. Gronau, R. (1986) Home production: a survey. In O. Ashenfelter and R. Layard (eds.), Handbook of Labour Economics, Vol. 1. North Holland, Amsterdam. Jara-Diaz, S.R. (1990) Valor subjetivo del tiempo y utilidad marginal del ingreso en modelos de partition modal. Apuntes de Ingenieria 39, 4150. Jara-Diaz, S.R. and Farah, M. (1987) Transport demand and user's benefits with fixed income: the goods/leisure trade-off revisited. Transportation Research 21B, 165-170. Jara-Diaz, S.R, and Ortuzar, J. de D. (1989) Introducing the expenditure rate in the estimation of mode choice models. Journal of Transport Economics and Policy XXIII, 293-308. Jara-Diaz, S.R. and Videla, J. (1989) Detection of income effect in mode choice: theory and application. Transportation Research 23B, 393-400. Johnson, B. (1966) Travel time and the price of leisure. Western Economic Journal 8, 135-145. Juster, F.T. (1990) Rethinking utility theory. The Journal of Behavioural Economics 19, 155-179. McFadden, D. (1981) Econometric models of probabilistic choice. In C. Manski and D. McFadden (eds.), Structural Analysis of Discrete Data: With Econometric Applications. The MIT Press, Cambridge, Mass. Oort, C. (1969) The evaluation of travelling time. Journal of Transport Economics and Policy HI, 279-286. Train, K. and McFadden, D. (1978) The goods/leisure trade-off and disag-


33

gregate work trip mode choice models. Transportation Research 12, 349-353. Truong, P. and Hensher, D.A. (1985) Measurement of travel time values and opportunity cost from a discrete-choice model. The Economic Journal 95, 438-451. Viton, P. (1985) On the interpretation of income variables in discrete choice models. Economic Letters 17, 203-206. Winston, G.C. (1987) Activity choice: a new approach to economic behaviour. Journal of Economic Behaviour and Organisation 8, 567-585.

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3

Causal Analysis in Travel Behaviour Research: A Cautionary Note Ram M. Pendyala

Abstract Causal models are increasingly being used in travel demand analysis as they offer greater insights into cause-and-effect relationships underlying travel behaviour. However, these modelling efforts usually assign the same causal relationships to the entire population under study. The inherent assumption that the same causal structure governs the behaviour of the entire population may be incorrect, as human behaviour is known to exhibit considerable variation both within and between behavioural units. This chapter investigates the extent to which such structural heterogeneity can be captured by models. The study involves an experiment in which model estimation is performed on simulated structurally heterogeneous data sets. The effect of structural heterogeneity on properties of parameter estimates is examined in detail. Based on the results of the experiment, the chapter concludes with a cautionary note on the estimation of causal models in travel behaviour research.

Introduction The development and implementation of effective transportation planning measures requires the ability to accurately model and forecast travel demand as it evolves over time. In particular, a planner needs to model cause-and-effect relationships governing travel behaviour to be able to answer "what-if" scenario based questions for alternative transportation policies. This need is being increasingly felt by transportation planners around the world as the focus of planning has shifted from facility expansion to the application of effective travel demand management strategies. An understanding of causal relationships underlying travel behaviour

36


would potentially better facilitate the evaluation of planning projects, trip reduction measures, alternative fuel vehicles, and advanced technologies. For this reason, causal analysis has been gaining increasing attention in travel behaviour research. Causal analysis has interested researchers in various fields of science and engineering for several reasons. Causal relationships may be considered to represent the most fundamental understanding of a phenomenon under study. Potentially, knowledge of causal relationships allows one to formulate policies and influence behaviour in an effective and more predictable way (Bagozzi, 1982; Blalock, 1985). Causal modelling efforts were first employed in biological (e.g. Wright, 1934) and psychosocial sciences (e.g. Greenwood, 1945). Due to the evident attractiveness of causal modelling, these initial efforts were soon followed by extensive applications in numerous other fields of research. The widespread application of causal models was further facilitated through advances in statistical and econometric estimation techniques coupled with improvements in computational resources. For example, causal models have been applied in sociology to study causes of crime (Ahn, 1985), in economics to study national income and expenditures (Sahni and Singh, 1984), in marketing to study consumer purchasing behaviour (Bagozzi, 1982), and in psychology to track causes of juvenile delinquency (Belson, 1985). Applications of causal models in the pure sciences include investigations into causes of various diseases in medical research (Elwood, 1988), factors contributing to plant and animal growth in botany and zoology (Braakhekke, 1980), and determinants of manufacturing system productivity in engineering (Horn, 1990). Causal modelling soon found application in transportation engineering and planning. In travel behaviour research, causal models have been applied to study various aspects of travel behaviour. Goodwin (1987) studied relationships between changes in family structure and public transport use. Kitamura (1987) analysed causal relationships between car ownership levels and car utilisation. Kitamura (1989) used the log-linear modelling method to estimate causal linkages between car ownership and transit use. Lyon (1984), Golob (1989), Golob and Meurs (1987) and van Wissen and Golob (1990) have used various econometric and sociometric methods to analyse causal relationships among trip generation, mode choice, attitudes and perceptions, travel time expenditures, and travel distances. These represent but a small fraction of the applications of causal modelling in travel behaviour research. A more extensive and thorough review can be found in Pendyala (1992).

Causal Analysis in Travel Behaviour Research

37

This paper addresses an important issue concerned with the specification and estimation of causal models. This issue is related to the behavioural paradigm that different behavioural units within the same population may be driven by differing causal mechanisms. More often than not, especially when dealing with human behaviour, the same data set may have several causal structures governing relationships among variables. However, causal modelling efforts assign the same set of causal relationships to all behavioural units in the study population. In other words, a strong assumption that all behavioural units are driven by the same causal mechanism is inherent in causal modelling. This assumption is questionable considering that human behaviour tends to show considerable variation both within and between behavioural units (Middlebrook, 1974). In this paper, the variation in causal relationships between behavioural units will be termed "structural heterogeneity". Examples of such structural heterogeneity can be found in numerous areas of behavioural research. In travel behaviour research, household car ownership may be driven by trip generation for a portion of the population, while trip generation may be driven by car ownership for another portion of the population. These represent causally opposite decision-making mechanisms. In addition, there may be a third portion of the population for whom there is no causal relationship between trip generation and car ownership at all. If all three possible sets of relationships are embedded in a data set, can model systems represent such structurally heterogeneous populations and if so, to what degree of accuracy? What is the effect of the presence of structural heterogeneity on the statistical properties of parameter estimates? The fundamental questions raised above need to be answered before causal models can be applied effectively in behavioural research. This paper investigates the impacts of structural heterogeneity on causal model estimability and statistical properties of parameter estimates. An experimental approach is adopted to make this assessment. Model systems are estimated on structurally heterogeneous data sets that are simulated (generated) based on known relationships. Model estimates are compared against true parameter values to assess the effects of structural heterogeneity on model estimation. In the next section, the problem statement is described and illustrated in greater detail. This is followed by a description of the simulation experiment. The fourth section presents the results of the simulation experiment while the fifth and final section provides concluding remarks.

38


Structural Heterogeneity in Travel Behaviour Human travel behaviour is very complex and is characterised by a substantial amount of randomness. (Hanson and Huff, 1982). It is very likely that different behavioural units are driven by different causal mechanisms. In fact, the causal mechanism underlying behaviour may vary for the same behavioural unit depending on the circumstances in which a travel decision has to be made. However, models tend to assign the same causal relationships to all behavioural units. This may not be appropriate given that decision-making rules differ across behavioural units and over time (Middlebrook, 1974). There are several examples of such possible structural heterogeneity in travel behaviour. For example, mode choice and destination choice are inter-related travel characteristics and the direction of causation between them may be different for different people. For some population units, mode choice may be driven by destination choice. As an example, consider a shopping trip. Choosing to shop at a place close to home may make the person opt to use the bicycle or walk, while choosing to shop at a store far away from home may entail using the car. In this case, it is clear that destination choice preceded and lead to a choice of mode. In contrast, some other population units of the study area may be driven by mode choice. Suppose a person chooses to drive because a car is available, it is night and unsafe to walk, or needs to trip chain with other trip purposes. Then, this person may choose to shop at a location farther from home because the car is being used anyway. On the other hand, if the person is taking an evening stroll, the shop close to home will be chosen. In this case, choice of destination is driven by the choice of transport mode. Two more types of relationships are possible. The first would be one in which mode and destination are chosen simultaneously. In this case, the person would make the decisions to go to a store far away and use the car simultaneously. Finally, mode choice and destination choice may not be related at all for some people. A person who is handicapped may choose to drive for reasons of safety and convenience. However, this person may choose to shop at a place closer to home because it would be cumbersome to drive long distances. In this case, clearly mode choice and destination choice are not related in reality, and any relation that does exist between them is purely spurious because the choices were actually influenced by the common variable "being handicapped". Similarly, other examples of relationships that may differ across behavioural units and over time may be conjectured. Car ownership, mode


39

choice, travel destination choice, route choice, and trip frequency are all aspects of travel behaviour, the relationships among which may be different for different people. This may have serious implications for forecasting and impact assessment of travel demand management strategies. Assuming a certain causal relationship for all behavioural units, when in fact, only a certain segment follows that relationship, may lead to highly erroneous forecasts. Impact assessments performed on the basis of such strong assumptions may be inaccurate and implementation of a travel demand strategy on a large scale may yield results that are very different from what it was originally intended to provide. Considering the importance of this issue, the question that merits immediate attention is whether models can reveal true causal relationships in the presence of structurally heterogeneous population segments. Also, the impact of structural heterogeneity on statistical properties of parameter estimates needs to be assessed. In the next section, a simulation experiment aimed at addressing these questions is described in detail.

Experimental Design and Procedure This section describes a simulation experiment aimed at assessing the extent to which models can replicate causal relationships in structurally heterogeneous samples. Such an assessment would be best accomplished by comparing model estimates against true parameter values. However, as true parameter values are never known a priori in real-world data sets, it was considered most appropriate to adopt an experimental approach. In the experiment, two model systems representing causally opposite structures are developed. Data sets generated using each structure are then pooled to form a structurally heterogeneous sample, such that the unified data set has two completely different underlying causal relationships. One of the two underlying model systems is estimated on the pooled data set to assess the accuracy with which models can capture causal relationships in the presence of structural heterogeneity.

Specification of Causal Structures Quite often, behavioural phenomena are characterised by several variables interacting among each other simultaneously (Maddala, 1983). In travel demand analysis, car ownership, mode choice, trip generation, vehicle

40


miles travelled, and travel times are all interdependent variables describing travel behaviour. For this reason, analysis of travel behaviour has increasingly involved the specification and estimation of simultaneous equations model systems. In a simultaneous equation model system, relationships among several endogenous (dependent) and exogenous (explanatory) variables can be explicitly incorporated. This allows the analyst to model behavioural phenomena in a unified and coherent framework. Recent advances in econometric and statistical methods coupled with improvements in computational resources have further contributed to the widespread application of simultaneous equations models in travel behaviour research. In this paper, two simultaneous equations model systems are used to generate the different causal structures. Each model system consists of two equations, having two endogenous variables and two exogenous variables. For ease of estimation, a linear system is considered. However, the results of this paper can be extended to non-linear systems as endogenous variables may be regarded as latent variables (to allow for discrete choice, censored, or truncated distributions). Consider two endogenous variables describing travel behaviour, say, car ownership and residential location choice. Also consider two exogenous variables, household size and household income. There are two plausible sets of relationships that may apply to different households: (A) Car ownership is predetermined. Residential location choice is dependent upon car ownership (say, a household decides to live in a neighbourhood well served by transit if its car ownership is low). (B) Residential location choice is predetermined. Car ownership is then determined based on the type of residential location (say, if the neighbourhood is not well served by transit, the household may choose to acquire additional cars). Both of the above are quite plausible relationships. It is very possible that some households follow structure (A) while other households follow structure (B). In addition, two other sets of plausible relationships exist. First, car ownership and residential location may be determined simultaneously, and second, car ownership and residential location may not be related at all. As the objective of this paper can be accomplished by just considering two different causal structures, only structures (A) and (B)


41

described above are used to produce a structurally heterogeneous data set. Causal structures (A) and (B) may be represented using systems of simultaneous equations. Let the endogenous variables be represented as, YI = Car ownership Y2 = Residential location choice and the exogenous variables as, Xi = Household size X2 = Household income. Then, causal structure (A) can be mathematically represented as,

where yl5 S1? /32 and 52 are structural model coefficients; e\ and e2 are random structural error terms distributed as bivariate normal (0, Se) with covariance matrix as follows:

Let causal structure (A) depicted above represent the true causal relationships underlying travel behaviour for a portion of the sample. The following values are assigned to various parameters noted above (these represent true values against which model estimates are to be compared): yt = 2.0 o-ei = l

5! = 0.3 0-^ = 1.2

& =-1.5 o- 61€2 =1.08

52 = 0.25

Similarly, causal structure B may be mathematically represented as,

where 01, 5t, y2, and 52 are structural model coefficients; €i and e2 are

42


random structural error terms distributed as bivariate normal (0, Se) with covariance matrix as specified previously. Let causal structure (B) depicted above represent the true causal relationships underlying travel behaviour for the remaining portion of the sample under study. The following values are assigned to various parameters for data generation purposes: )8i=-1.5 a€l = 1

72 = 2.0 o-C2 = 1.2

Si = 0.25 o-ei£2 = 1.08

^ = 0.25

A close similarity is maintained in specifying the true parameter values for structures (A) and (B). This helps in achieving efficiency in the data generation process and allows a more meaningful comparison between estimated and true values. The only difference between the two structures lies in the direction of the causal link between the endogenous variables, YI (car ownership) and Y2 (residential location). Also, for purposes of data generation, it is assumed that the exogenous variables, Xi and X2, are distributed as bivariate normal with the following parameterisation: /**! = 3

fiX2 = 38

axi = 2.5

crX2 = 6

pxlX2 = 0.7

The model specification and parameterisation described in this section allows the generation of structurally heterogeneous data sets on which model systems can be estimated. The experimental procedure is briefly outlined in the next section.

Experimental procedure The procedure followed in performing the simulation experiment can be outlined as follows: (i)

A bivariate normal random vector of exogenous variables (Xi,X2) is generated using the parameters specified in the previous section. This is repeated 500 times to get a sample size of 500.

(ii)

A realisation of a bivariate normal random vector of error terms (e l5 e2) is generated using the specified parameter values. This is done for each of the 500 cases.

Causal Analysis in Travel Behaviour Research (iii)

43

Using the model specification in equation (1), endogenous variables YI and Y2 are computed and generated for each of the 500 cases. In this way, a complete data set with causal structure (A) as its underlying basis is obtained. 100 such data sets are generated.

(iv) Model structure (A) is estimated (using full-information maximum likelihood techniques) on each of the 100 data sets. Arithmetic means of parameter estimates are computed, tabulated, and compared against the true values. (v)

Steps (i) through (iii) are repeated for structure (B).

(vi)

From each of the 100 data sets of causal structure (A), a random sample of 250 is drawn. Similarly, from each of the 100 data sets of causal structure (B), a random sample of 250 is drawn.

(vii) The samples are pooled such that 100 data sets of sample size 500 each are generated. All data sets have 250 sample units drawn from causal structure (A) and 250 sample units drawn from structure (B). This yields 100 structurally heterogeneous data sets. (viii) Model structure (A) is estimated on each of the 100 structurally heterogeneous data sets. Arithmetic means of parameter estimates are compared against true values to assess the degree of accuracy with which models can capture structurally heterogeneous causal relationships.

Results of Simulation Experiment Full-information maximum likelihood (FIML) methods, implemented via LISREL structural equations software (Joreskog and Sorbom, 1987), were used for model estimation. A review of these methods can be found in Greene (1990). This section provides results of the model estimation efforts on structurally homogeneous and structurally heterogeneous data sets. First, the model system describing structure (A) was estimated on the 100 data sets generated purely based on structure (A). These may be considered structurally homogeneous data sets. One would expect models

44


to provide accurate parameter estimates under these conditions. Table 1 provides results of this effort. It can be seen that, in the case of structural homogeneity, models do capture and replicate true relationships among variables. Percentage errors in parameter estimates are extremely small. Table 1 Estimation of causal structure (A) on homogeneous data set: comparison of true and estimated parameter values Parameter

True value

Estimated value

Error in estimate (%)

True standard error

Estimated standard error

Error in standard error

jSz 7! 8j §2

-1.50 2.00 0.30 0.25

-1.499 2.000 0.298 0.248 1.004 1.438 1.084

0.0 0.0 -0.6 -1.0 0.4 -0.1 0.4

0.0156 0.0241 0.0105 0.0164 0.0645 0.1086 0.0822

0.0154 0.0256 0.0111 0.0169 0.0639 0.0970 0.0742

-1.8 6.3 6.4 3.0 -1.0 -10.7 -9.8

3

General indicators

Non-mobile people (%) Average travel time per day (min) Average number of trips per day (per inhabitant) Average number of trips per day (persons with mobility) Average number of noncompulsory trips per day

Porto (central)

Suburban municipalities

Total Porto

Lisbon (central)


Total Lisbon (1st phase)

§

§ ft"

14.6 75.5 2.30

18.4 79.5 1.99

17.1 78.1 2.10

20.5 83.4 2.23

22.4 95.4 2.11

21.5 90.0 2.17

2.69

2.44

2.53

2.80

2.72

2.76

1.06

0.96

1.00

0.79

0.62

0.70

•**

5' °^

TO 00 5? TO*

^

Mobility Surveys in Lisbon and Porto

287

Car ownership Table 3 shows the values relative to some car ownership indicators, not only for the number of cars per thousand inhabitants, but also about the distribution of families according to their level of motorisation. It is interesting to note that in both areas, families in the central city are less motorised than those living in the suburbs, the difference being much more visible in the case of Lisbon. A normal explanation for this would be that the public transport networks are much denser in the centre, so that people have less need of a car. But, we believe the main reason is different. The population of these central cities has a considerably larger stratum of old people than is the case for the suburbs. In the case of Porto, the percentage of population over 65 years-of-age is 14.8 percent for the central city and 9.0 percent for the suburbs. In Lisbon the difference is even larger, 18.4 percent in the central city versus 9.4 percent in the suburbs. In a global comparison, this part of the Lisbon area has a slightly higher car ownership level than Porto (about 4 percent difference).

Modal split Table 4 presents modal split figures for both cities. In Porto central, shortrange public transport is the market leader, being used for over 50 percent of the trips made. The global value for the Porto area is slightly smaller, but nonetheless clearly ahead of the other modes. In the suburbs, the lost share is captured by walking, bicycle and by the private car. Long-distance public transport has a very low share in all of this area. In Lisbon, although short-range public transport is still the market leader, its difference to the others is considerably smaller, the corresponding share going to private cars and longer-range public transport (subregional transport in most cases). It is also worth noting the comparatively high value of 'Park & Ride' for the suburban municipalities of Lisbon. This is clearly due to the existence of the Metro, but also probably due to the suburban railways, which play a much stronger role in this region than in Porto.

288 Travel Behaviour Rese

Table 3 Household car ownership indicators Families with

No car (%) One car (%) Two or more cars (%) Cars per 1000 inhabitants

Porto (central)


Total Porto

Lisbon (central)



40.5 40.8 18.7 257

33,.8 44,,7 21..5 238

36.2 43.3 20.5 243

38,.7 45,.6 15,.7 242

25,,8 54,,8 19,.4 254

31.8 50.5 17.7 249

^ & S' Crq ^

ST

Table 4 Distribution of transport modes per trip Modal split

On foot/bicycle (%) Private car/motor bike (%) Short range transit (%)* Medium + long range transit (%) Taxi (%) Transit + private car (%)

Porto (central)

24.7 22.4 51.6 0.3 0.5 0.5


28.2 29.1 40.9 0.8 0.5 0.5

* Short range transit denned as having a trip duration under one hour.

Total Porto

26.9 26.6 44.9 0.6 0.5 0.5

Lisbon (central)

22.1 29.0 42.0 4.4 1.4 1.1


22.6 32.6 32.0 7.8 0.2 4.8


22.4 30.8 36.8 6.2 0.8 3.0

^ 2"

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