Abstract
Route choice is one of the most critical passenger behaviors in public transit research. The utility maximization theory is generally used to model passengers’ route choice behavior in a public transit network in previous research. However, researchers have found that passenger behavior is far more complicated than a single utility maximization assumption. Some passengers tend to maximize their utility while others would minimize their regrets. In this paper, a schedule-based transit assignment model based on the hybrid of utility maximization and regret minimization is proposed to study the passenger route choice behavior in an urban rail transit network. Firstly, based on the smart card data, the space-time expanded network in an urban rail transit was constructed. Then, it adapts the utility maximization (RUM) and the regret minimization theory (RRM) to analyze and model the passenger route choice behavior independently. The utility values and the regret values are calculated with the utility and the regret functions. A transit assignment model is established based on a hybrid of the random utility maximization and the random regret minimization (RURM) with two kinds of hybrid rules, namely, attribute level hybrid and decision level hybrid. The models are solved by the method of successive algorithm. Finally, the hybrid assignment models are applied to Beijing urban rail transit network for validation. The result shows that RRM and RUM make no significant difference for OD pairs with only two alternative routes. For those with more than two alternative routes, the performance of RRM and RUM is different. RRM is slightly better than RUM in some of the OD pairs, while for the other OD pairs, the results are opposite. Moreover, it shows that the crowd would only influence the regret value of OD pair with more commuters. We conclude that compared with RUM and RRM, the hybrid model RURM is more general.
Highlights
Analysis of travelers’ route choice behavior is very important for daily urban rail transit operation
The majority of existing studies of traveler’s route choice behavior are based on the random utility maximization (RUM) [1]. These RUM models assume that when faced with a number of travel choice options, a traveler is rational enough and he or she will choose the one that has the highest utility value according to the information which was obtained by the traveler
There are two basic types of the choice behavior assumptions: one is that the choice behavior of passengers is fully rational, which is mainly based on the theory of utility maximization, and the other is that the choice behavior of passengers is finite rational, which is mainly based on the theory of regret minimization
Summary
Analysis of travelers’ route choice behavior is very important for daily urban rail transit operation. The majority of existing studies of traveler’s route choice behavior are based on the random utility maximization (RUM) [1]. Some people tend to maximize their utility while others would minimize their regret This is obvious from the travelers’ behavior in route choice. The hybrid route choice behaviors of random utility maximization and random regret minimization are proposed and formulated to analyze the travelers’ route choice for an urban rail transit network. This research applies the regret to a schedule-based transit assignment by constructing a space-time expanded network from smart card data. This paper incorporates regret minimization and utility maximization into a transit assignment model to characterize travelers’ route choice behavior.
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