Research on train operation adjustments in urban rail transit based on passenger flow OD data

This study addresses the delays caused by urban rail transit trains, which lead to subsequent train delays and large-scale passenger congestion. It comprehensively considers both passenger and operator perspectives. By calculating passenger flow parameters based on origin-destination (OD) data, a multi-objective train operation adjustment model is constructed. This model aims to optimize the total deviation between scheduled and actual train operation diagrams, as well as the total waiting time of passengers at platforms. To account for the complexity of train operation constraints, rules for initializing feasible solutions are established, and the Particle Swarm Optimization (PSO) algorithm is improved, with critical solution processes designed. Finally, simulation experiments using actual cases are conducted to validate the effectiveness of the proposed train operation adjustment model and the improved PSO algorithm.