Modeling And Equilibrium Analysis Of Travel Behaviors Based On Bounded Rationality

Travelers’decision-making behaviors in trip directly influence the distribution of the urban traffic network flow. The researches on the travel behaviors are the foundation of urban traffic planning, traffic demand management, traffic network design, and so on. Within the frame of bounded rationality, this paper intends to analyze and model travel behaviors based on satisficing decision rule and cumulative prospect theory. The interaction laws between travelers choice behavior and the distribution of the urban traffic network flow were investigated. Various traffic demand management strategies were represented to relieve traffic congestion through guiding travelers’choice behaviors. Specifically, the main contents of the dissertation are summarized as follows:(1) Travelers’mode choice behaviors based on bounded rationality in the two-mode network were studied. The two-mode choice equilibrium model based on bounded rationality was proposed and the equilibrium solutions under best case, worst case and average case were analyzed, respectively. Under decision makers’ different risk preference (risk seeking, risk neutral and risk averse), the road toll and transit fare was researched. The min-max, min-min and minimize programming model for road toll optimization were proposed and solved by the compass search method, respectively. Numerical examples were presented to illustrate the influence of boundedly rational mode choice behavior and risk-based road toll strategies on the system total travel cost.(2) In the two-mode network, the travel demand was assumed to be uncertain and travelers were assumed to be boundedly rational. In the mode choice decision, travelers would fully consider the mean and variance of the travel cost, and might choose the travel mode with higher perceived travel cost in a reasonable threshold range. The bi-level programming for transit fare optimization was proposed, the upper-level objective was to minimize the mean of the total travel cost, whereas the lower-level problem was the mode choice equilibrium model based on risk preference and bounded rationality. Then, a heuristic algorithm based on sensitivity analysis approach was designed to solve the bi-level programming. Numerical examples were presented to illustrate the effect of demand uncertainty and bounded rationality on the modal share, transit fare and system total travel cost.(3) On the foundation of the classic bottleneck model, the earliest acceptance arrival time, the preferred arrival time and the work starting time were served as reference points, pricing models in a highway with bottleneck based on total travel utility were presented, commuters’ travel time disutility and relative utility of arrival time were considered simultaneously, the optimal time-varying toll scheme was given accordingly. Further, two single-step toll schemes and three double-step toll schemes were proposed, and the charging level, pricing time and tolling effect of each toll scheme were obtained. The single-step toll can eliminate at most more than1/2of the total queueing time and the double-step toll can eliminate at most more than2/3of the total queueing time.(4) The route travel time was assumed to follow normal distribution and the reference-dependent theory was used to study travelers’ route choice behaviors. The intrinsic utility and the gain-loss utility in travelers’ route choice decision were fully considered, the former was determined by the route travel time and the latter was jointly estimated by the route travel time and reference point. Then, the reference-dependent theory-based user equilibrium model for stochastic network was presented. On the other hand, the route travel time was assumed to be distribution-free and the cumulative prospect theory was used to study traveler’ route choice behaviors. Travelers always chose the routes with the maximum worst-case cumulative prospect value. Thus, the robust user equilibrium model under cumulative prospect theory was proposed. Finally, the influence of travelers’ risk aversion, loss aversion and reference dependence on the network equilibrium result was analyzed.