Abstract
Random events like accidents and vehicle breakdown, degrade link capacities and lead to uncertain travel environment. And whether travelers adjust route or not depends on the utility difference (dynamic rerouting behavior) rather than a constant. Considering travelers’ risk-taking behavior in uncertain environment and dynamic rerouting behavior, a new day-to-day traffic assignment model is established. In the proposed model, an exponential-smoothing filter is adopted to describe travelers’ learning for uncertain travel time. The cumulative prospect theory is used to reflect route utility and its reference point is adaptive and set to be the minimal travel time under a certain on-time arrival probability. Rerouting probability is determined by the difference between expected utility and perceived utility of previously chosen route. Rerouting travelers choose new routes in a logit model while travelers who do not choose to reroute travel on their previous routes again. The proposed model’s several mathematical properties, including fixed point existence, uniqueness, and stability condition, are investigated through theoretical analyses. Numerical experiments are also conducted to validate the proposed heuristic stability condition, show the effects of four main parameters on dynamic natures of the system, and investigate the differences of the system based on expected utility theory and cumulative prospect theory and with static rerouting behavior and dynamic rerouting behavior.
Highlights
Transportation network modeling mainly examines the nal equilibrium state for the past few decades on an explicit or implicit assumption that if equilibrium exists, it will occur. e assumption is very ideal; it was quite contrary to happen [1]
We investigate the e ects of the di erent rerouting behaviors
We analytically study the existence and uniqueness of its xed point and give a heuristic stable condition of the xed point. en several numerical experiments are conducted on the Nguyen–Dupuis network to validate the heuristic stability condition, to show the e ects of the four main parameters, to investigate the di erence between two utility frameworks called EUT and CPT and nally to show the di erence between travelers’ static rerouting behavior and dynamic rerouting behavior. e results show that: (1) Parameters, 0 and all a ect the stability of the dynamic system
Summary
Transportation network modeling mainly examines the nal equilibrium state for the past few decades on an explicit or implicit assumption that if equilibrium exists, it will occur. e assumption is very ideal; it was quite contrary to happen [1]. Guo et al [26] presented a link-based model to avoid the numeration of a large number of routes In these models, tra c ows are the result of travelers’ perceived travel costs by learning experienced travel costs. Most of the DTD models previously introduced do not address travel time uncertainty led by randomly degradable link capacity Meantime, they commonly assume that travelers always adjust their routes to pursue the optimal utility or that a xed proportion of travelers adjust their route considering their inertia. Is paper simultaneously considers the in uence of travel time uncertainty on route choice and travelers’ dynamic rerouting behavior (DR) to construct a more realistic DTD tra c assignment model.
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