Passenger route choice model and algorithm in the urban rail transit network

Abstract

Purpose: There are several routes between some OD pairs in the urban rail transit network. In order to carry out the fare allocating, operators use some models to estimate which route the passengers choose, but there are some errors between estimation results and actual choices results. The aim of this study is analyzing the passenger route choice behavior in detail based on passenger classification and improving the models to make the results more in line with the actual situations. Design/methodology/approach: In this paper, the passengers were divided into familiar type and strange type. Firstly passenger integrated travel impedance functions of two types were established respectively, after that a multi-route distribution model was used to get the initial route assignment results, then a ratio correction method was used to correct the results taking into account the transfer times, crowd and demand for seats. Finally, a case study for the Beijing local rail transit network is shown. Findings: The numerical example showed that it is logical to take passenger classification and the model and algorithm is effective, the final route choice results are more comprehensive and realistic. Originality/value: The paper offers an improved model and algorithm based on passenger classification for passenger route choice in the urban rail transit network.