Data-driven approach for solving the route choice problem with traveling backward behavior in congested metro systems

Authors

Chao Yu
Haiying Li
Xinyue Xu
Jun Liu

Document Type

Journal Article

Publication Date

2020

Subject Area

mode - subway/metro, place - asia, ridership - behaviour, ridership - demand, ridership - modelling, operations - crowding

Keywords

Metro system, Data driven, Bayesian, Traveling backward (TB), Route choice

Abstract

This study proposes a data-driven approach to understand traveling backward (TB) behavior while making a route choice in congested metros. First, TB behavior during route choice in overcrowded metros is defined and analyzed. Second, a hybrid model comprising three hierarchical Bayesian models is developed to describe the TB behavior. Third, a novel sampling method based on Hamiltonian dynamics is introduced to estimate the model parameters. Finally, a case study of Beijing metro is discussed to illustrate the effectiveness and robustness of the proposed model. The contributions of application of the proposed model to accurate demand assignment and passenger flow control for metro managers are demonstrated.

Rights

Permission to publish the abstract has been given by Elsevier, copyright remains with them.

Comments

Transportation Research Part E Home Page:

http://www.sciencedirect.com/science/journal/13665545

Recommended Citation

Yu, C., Li, H., Xu, X.,& Liu, J. (2020). Data-driven approach for solving the route choice problem with traveling backward behavior in congested metro systems. Transportation Research Part E: Logistics and Transportation Review, Vol. 142, 102037.