Research On Dynamic Route Choice Behavior Based On Prospect Theory

Route choice is a key problem in travel behavior research field, which providestheoretical foundations for the research of many other traffic issues. Existing routechoice research is generally based on the assumption that travelers are entirelyrational and usually make their decisions based on minimum disutility, maximumutility or reliability of the available alternatives, and seldom considers travelers’bounded rationality in their decision-makings. Around the issue of route choice, thispaper is intended to analyze and model travel behavior based on Prospect Theorywithin the frame of bounded rationality. The interaction laws between travelers’dynamic route choice behavior and traffic system’s macroscopic operationalperformances are also investigated. Reference points’ updating rules in sequentialtravel decision scenes are explored as well.Firstly, the state-of-art of route choice research and Prospect Theory’sapplication in travel behavior research is reviewed and summarized. Severalproblems and shortages in existing research are pointed out, followed by the maincontents and technical roadmap of the dissertation. Then around the applicability ofProspect Theory in travel behavior research, it is suggested that the key criterion liein whether the alternative attribute within traveler’s consideration involvesuncertainty or not. This conclusion provides guidance and foundation for the specificapplication of Prospect Theory in travel behavior research. In addition, some adviceon how to select a more suitable theory in actual research is given to avoid of severalexisting defects of Prospect Theory.Secondly, in consideration of the uncertainty of travel time and travelers’perception errors in traffic network simultaneously, a Stochastic User Equilibriummodel based on Cumulative Prospect Theory is formulated, in which travelers’endogenous reference points are set by an integration of travel time reliability andtravel time budget. Then the equivalent variational inequalities and algorithms forthe model are given accordingly under the condition of fixed and elastic traveldemand, followed by two numerical examples respectively to provide validity of the model and algorithms. By parameter sensitivity analysis, a study of the interactionlaws between travelers’ cognitive and psychological factors, such as reference point,risk appetite, loss aversion and perception errors, and the traffic network equilibriumis then conducted. Conforming more to travelers’ decision behavior in reality, thesuggested model can improve the accuracy of traffic demand forecasting.Thirdly, in order to apply Prospect Theory to dynamic traffic assignment inconsideration of the problem of departure time choice, a Stochastic Dynamic UserOptimum model based on Cumulative Prospect Theory is formulated, whichproperly handles the reference points of departure time and route choices focusingon commute trips in morning peak. After an equivalent variational inequality andalgorithm of the model are given, the influence of travelers’ dynamic route choicebehavior on time-varying traffic flow is analyzed through a numerical example. Thena set of dynamic travel reliability indicators are established for the purpose ofevaluating traffic network’s operational performance and service efficiency. Anillustration of the relationship between departure time choice and arrival reliabilitybased on the proposed numerical example is followed. The proposed model expandsthe concept of time-window and schedule delay well and overcomes the limitation ofentire rationality assumption in existing models dealing with departure time choice.Fourthly, in scenarios of different information provision, four dynamical trafficsystem models are formulated based on Cumulative Prospect Theory, in which fourevolutionary dynamics, namely the replicator, the best response, the Smith and theBNN dynamics, are introduced to investigate travelers’ day-to-day route choicegame learning behavior respectively. Then the dynamical traffic flow evolution andachievements of User Equilibrium are presented through two numerical examplesunder different initial states and distinct reference points’ updating rules. By anintegration of equilibrium and evolution analysis methods, this paper reveals themechanisms of traffic flow evolution as well as the processes and conditions oftraffic network equilibrium, and enriches and develops traditional traffic assignmenttheories.Finally, a sequential travel decision learning model is formulated based onCumulative Prospect Theory and the Bayes Law. After a deep analysis of thecharacters of sequential travel decisions as well as the connotations and affectingfactors of reference point, a route choice experiment is designed to investigate theupdating rules of reference point. The introduction of experimental research method not only provides notion and reference for empirical study of travel behavior, butalso creates conditions for Prospect Theory’s application in practice.By an integration of normative research and empirical analysis, this paperapplies behavioral science to travel behavior analysis and modeling based on rationalbehavior assumptions and research method. The suggested models, algorithms andconclusions can provide theoretical foundation for further research of traffic issues,such as traffic assignment, dynamic route guidance, congestion pricing and regionaltraffic restriction, etc.