Data-driven approach for solving the route choice problem with traveling backward behavior in congested metro systems
mode - subway/metro, place - asia, ridership - behaviour, ridership - demand, ridership - modelling, operations - crowding
Metro system, Data driven, Bayesian, Traveling backward (TB), Route choice
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.
Permission to publish the abstract has been given by Elsevier, copyright remains with them.
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.