Dynamic passenger flow assignment in urban rail transit networks based on AFC data

In order to study the optimization of urban rail transit train schedules, this research aims to derive dynamic passenger flow data for network sections over time based on time-varying OD (origin-destination) passenger flow data. AFC (Automatic Fare Collection) data is divided into several groups of OD passenger flow data based on a set time granularity. By utilizing passenger travel time parameters derived from AFC data, as well as the actual inter-station train running times, and considering the impact of network passenger flow on travel time, a congestion coefficient is applied to the path cost in order to describe the travel time cost of passengers. The Method of Successive Algorithm (MSA) is employed to dynamically assign passenger flow for each time segment of the 4-hour calculation period, using a 5-minute granularity. The results of the multi-dimensional analysis of dynamic passenger flow assignment show that: (1) In large-scale networks, the efficiency of passenger flow assignment for high passenger volumes is at least 58.34 % higher than before the improvement, with each iteration’s convergence progress error not exceeding 3 % for different time periods. (2) While ensuring the output path results and quantities are consistent, the efficiency is significantly improved compared to traditional path search algorithms. (3) The introduction of the transfer count in the objective function improved the optimal objective function value by 5.49 % compared to the generalized path cost function alone.