prospect theory in transportation

  • View
    8

  • Download
    1

Embed Size (px)

DESCRIPTION

behavior and decision making in transportation

Text of prospect theory in transportation

  • ob

    s

    ty on aalitansg u, orcho

    these theories and models, and illustrates their application using examples of empirical research in travelbehavior research. Arguing that some basic assumptions underlying these theories may not mimic the

    Introduction

    The analysis and modeling of human ching has a long history in travel behavior re

    deringple ches forecute t

    vidual and household choice and decision-making processes. Untilthe mid 1970s, spatial interaction and entropy-maximizing mod-els, based on the theory of social physics, dominated the eld(Wilson, 1974; Batty, 1976). Later, random utility theory(McFadden, 1974) and psychological choice theory (Luce, 1959)led to the formulation and application of many discrete choice

    oment of choice,le individudge aboutdecision-m

    under conditions of certainty.In reality, however, the assumption that attribute valu

    certain is not very realistic. When leaving home, individuals willnot be certain about their arrival time to the intended destinationas travel times exhibit inherent uctuation. When choosing publictransport, travelers may not be sure whether they will have a seatas demand and therefore occupancy fundamentally varies on aday-to-day basis. When choosing a place to park their car, driversmay face unexpected queues, and parking garages may be full.

    Corresponding author. Tel.: +31 402474527.E-mail address: s.rasouli@tue.nl (S. Rasouli).

    Travel Behaviour and Society 1 (2014) 7990

    Contents lists availab

    ou

    w.demographics is crucial in evaluating social effects and equity oftransport investment and trafc management scenarios.

    Over the last decades, the travel behavior research communityhas applied a variety of theories and modeling approaches to indi-

    the attributes of their choice alternatives. At the mthe values of these attributes are invariant, whiassumed to hold perfect and complete knowleattributes. All these models relate to choice andhttp://dx.doi.org/10.1016/j.tbs.2013.12.0012214-367X/ 2014 Hong Kong Society for Transportation Studies Published by Elsevier Ltd. All rights reserved.als aretheseaking

    es areroute to arrive at the chosen destinations, etc. Moreover, modelingconsumer response to exogenous policies is essential in assessingthe impact and effectiveness of transportation policies, capturedin terms of changing attributes of the choice alternatives ofinterest. Finally, understanding how choice behavior co-varieswith genders, income categories, age groups and other socio-

    popular. In parallel, but less pertinent, researchers have alsoapplied rule-based models to capture decision heuristics (e.g.,Arentze et al., 2000).

    Regardless of the modeling approach and the underlying theoryof choice and decision-making, these models have in common theassumption that decision-makers have perfect knowledge aboutshould not come as a surprise, consivel demand forecasting is how peoactivities, decide on the departure tima transport mode, decide where to exquintessence of day-to-day activitytravel behavior, the paper is completed by reecting on the limita-tions of these models and identifying avenues of future research.

    2014 Hong Kong Society for Transportation Studies Published by Elsevier Ltd. All rights reserved.

    oice and decision-mak-search. This statementthat the essence of tra-oose to participate inthese activities, chooseheir activities, choose a

    models (Hensher, 1981; Ben-Akiva and Lerman, 1985). Soon themultinomial logit model became the working horse of the profes-sion, to be complemented by various more advanced, less stringentmodels, such as the nested logit model and generalized extremevalue models, which avoided some of the rigorous assumptionsunderlying the multinomial logit model. Lately, the mixed logitmodel (Train, 2003), MDCV model (Bhat, 2005) and hierarchicalchoice models (Walker and Ben-Akiva, 2002) have become ratherApplications of theories and models of chunder conditions of uncertainty in travel

    Soora Rasouli , Harry TimmermansEindhoven University of Technology, Urban Planning Group, Eindhoven, The Netherland

    a r t i c l e i n f o

    Article history:Available online 27 January 2014

    a b s t r a c t

    The overwhelming majoriindividuals choose betweecan be questioned as in reimportant therefore that trand predict decision-makinadopt theories and modelstheir applicability to travel

    Travel Behavi

    journal homepage: wwice and decision-makingehavior research

    f models in travel behavior research assume implicitly or explicitly thatlternatives under conditions of certainty. The validity of this assumptiony urban and transportation networks are in a constant state of ux. It isportation researchers develop relevant approaches and models to analyzender conditions of uncertainty. Transportation researchers have tended toiginally developed in social psychology and decision sciences, and exploreice behavior. This invited review paper summarizes the most important of

    le at ScienceDirect

    r and Society

    elsevier .com/locate / tbs

    MOHAMMADHighlight

    MOHAMMADHighlight

    MOHAMMADHighlight

  • When choosing a route, drivers can never be sure about the con-gestion situation. Even if the chosen route is normally not con-gested, an accident or sudden adverse weather conditions maycause signicant delays.

    Thus, the state of the transportation system and the urbanenvelope are inherently uncertain. Consequently, decision-makersalways face conditions of uncertainty when choosing departuretimes, activities, destinations, transport modes, routes, etc. In thatsense, it is surprising that applications of theories and models of

    and management, and suggest some avenues of future research.un f mn;u pn; j 1; :::; J 4

    80 S. Rasouli, H. Timmermans / Travel BehaNotation

    Most models in travel behavior research on choice and decision-making assume that the probability of choosing a particular choicealternative is some function of the attributes of the choice alterna-tives and a set of socio-demographic variables. The position of thechoice alternatives on these variables is represented by a single va-lue. This represents choice and decision making under conditionsof certainty. In case of choice and decision making under uncer-tainty, the characterization of the choice alternatives is capturedin terms of probability distributions. It implies that individualsare or cannot be sure about the exact state of the choice alternativealong these uncertain dimensions or about the outcome of his deci-sions. This is the realm of choice and decision-making under con-ditions of uncertainty.

    To create an overall framework for the various models in an at-tempt to allow the researcher to compare the various approacheswith classic discrete choice model, in this section we will rstintroduce some notation. Expanding the notation suggested byLiu and Polak (2007), assume that each decision maker i is facedwith a set C = {sn; C fSn n Ng of N risky choice alternatives

    1 These three theories are just a small subset of theories and models of choicebehavior under uncertainty. For example, Hey and Chris (1997) list the followingadditional theories that were motivated by the inability of expected utility theory toexplain observed behavior. Allais (1952) theory, Anticipated Utility theory, Cumu-lative Prospect theory, Disappointment theory, Disappointment Aversion theory,Implicit Expected (or linear) Utility theory, Implicit Rank Linear Utility theory,Implicit Weighed Utility theory, Lottery Dependent Expected Utility theory, Mach-inas Generalised Expected Utility theory, Perspective theory, Prospective Referencetheory, Quadratic Utility theory, SSB theory, and Yaaris Dual theory. Some of thesetheories are special cases or generalisations of those discussed in this paper; othersdecision making under conditions of uncertainty are relativelyscarce in travel behavior analysis. Moreover, the majority of stud-ies, albeit also small in number, are concerned with uncertainty orvariability in the transportation system (Rasouli and Timmermans,2012a, 2012b), but do not address how individuals make decisionswhen facing such uncertainty and how it affects their activitytravel decisions. Considering the inherent uncertainty in the stateof the transportation system, the formulation and application of(improved) models of decision-making under conditions ofuncertainty should be a eld of research of high priority in travelbehavior research. This paper is meant to provide readers withsome basic background information and a state of the art overview.

    In particular, we will discuss the principles and applications ofexpected utility theory, (cumulative) prospect theory and regrettheory as these models have dominated the scarce literature in tra-vel behavior analysis on decision-making under uncertainty.1 Ineach section, we will rst discuss the basic assumptions and speci-cations of the model and some variations, and then discuss selectedresults of empirical studies. In addition to providing a state-of-the-art overview, we will also reect on the appropriateness andlimitations of these models to applications in transportation planningare based on different concepts.2 Although there are differences between risk and uncertainty, we will use the

    terms interchangeably in the present paper.i ij i j

    Expected utility theory

    Principles

    Expected utility theory can be traced back to the work of Ber-noulli in 1738 to solve the famous St. Petersburg paradox. This par-adox concerns the problem how much a single gambler should paya casino to enter a game in which he would toss a fair coin, doublesa start gain of 1 unit every time a head appears, and the game endsthe rst time a tail appears. Thus, the gambler would win 2k1

    units if the coin is tossed k times