Abstract
In this paper, route free-flow travel time is taken as the lower bound of route travel time to examine its impacts on budget time and reliability for degradable transportation networks. A truncated probability density distribution with respect to route travel time is proposed and the corresponding travel time budget (TTB) model is derived. The budget time and reliability are compared between TTB models with and without truncated travel time distribution. Under truncated travel time distribution, the risk-averse levels of travelers are adaptive, which are affected by the characteristics of the used routes besides the confidence level of travelers. Then, a TTB-based stochastic user equilibrium (SUE) is developed to model travelers’ route choice behavior. Moreover, its equivalent variational inequality (VI) problem is formulated and a route-based algorithm is used to solve the proposed model. Numerical results indicate that route travel time boundary produces a great influence on decision cost and route choice behavior of travelers.
Highlights
Most studies indicate that travel time reliability (TTR) is an essential travel decision cost of travelers [1,2,3]
The classical user equilibrium (UE) principle assumes that travelers are risk-neutral and their decision behavior only depends on the mean travel time in deterministic transportation networks
By incorporating simultaneously the reliable and unreliable sides, the mean-excess traffic equilibrium (METE) model was developed by Chen and Zhou (2010) [13]
Summary
Most studies indicate that travel time reliability (TTR) is an essential travel decision cost of travelers [1,2,3]. The classical user equilibrium (UE) principle assumes that travelers are risk-neutral and their decision behavior only depends on the mean travel time in deterministic transportation networks. In order to model travelers’ route choice behavior under uncertainty, a variety of traffic equilibrium models incorporating TTR are developed [7,8,9,10,11]. Chen et al (2011) further introduced the perception errors of travelers to the METE model and proposed the stochastic mean-excess traffic equilibrium (SMETE) model [14] In this case, the perceived travel time distribution is derived to model travelers’ route choice behavior rather than the actual one. The TTB-based SUE models are developed to model the impacts of route travel time boundary on travelers’ route choice.
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