Optimization of Train Operation Plans for Urban Rail Transit Based on Platform Crowding Degree

In order to enhance passenger travel experience, align transportation capacities with demands, and alleviate platform overcrowding at metro stations, this paper proposes a methodology for optimizing train operation plans based on platform crowding degree in rail transit systems, as a result of studies focusing on optimizing multi-route train operation plans based on historical passenger flow demands and automatically scheduling multi-route train operation for these systems. First, a modeling analysis was performed using historical sectional passenger flow and origin-destination (OD) passenger flow data, prioritizing platform overcrowding degree. A multi-objective integer planning model was constructed for the multiple-route operation of trains, aiming to minimize the maximum platform crowding degree and achieve the optimal match between train capacities and transportation volumes. Next, a genetic algorithm was utilized to derive the optimal operation plan through a solving process, and this optimal plan was integrated into subsequent operation scheduling, facilitating the automatic compilation of multi-route operation schedules. Finally, the effectiveness of the proposed approach was validated through simulations based on historical passenger flow data from a rail transit line. The simulation results indicated that the automatically-compiled multiple-route operation schedules based on this method lowered the maximum platform crowding degree by 20.6%, compared to manually-compiled ones, highlighting the algorithm's value in practical applications.