Destination-to-gate assignment to mitigate congestion-related risks in oversaturated metro lines: A new passenger flow control strategy

Abstract

The accumulation of large passenger flows at metro stations often poses congestion risks to urban rail systems, including an increased likelihood of accidents (e.g., slips, trips, and falls) and train departure delays. However, during peak hours, the priority boarding rights of passengers at upstream stations often lead to congestion and overcrowding at downstream popular stations. We propose a new passenger flow control strategy to address this issue, namely destination-to-gate assignment. This approach assigns specific gates to destinations served by the station, enabling passengers to board the appropriate train carriages. This strategic allocation facilitates a more even distribution of passengers, reducing congestion and enhancing spatial equity in passenger travel, thereby mitigating operational risks associated with overcrowding. For the problem of interest, we propose a nonlinear integer programming model to optimize the destination-to-gate assignment, aiming to simultaneously minimize risks related to passenger crowding and waiting times. The model adopts a first-come, first-served (FCFS) boarding rule to accurately capture the dynamic nature of passenger flow while considering the capacity limitations of train carriages. Leveraging the model’s characteristics, we employ a set of linearization methods to equivalently transform it into a mixed-integer linear programming (MILP) model. To address the computational challenges posed by real-world scale, we develop a customized heuristic algorithm that uses Variable Neighborhood Search (VNS) combined with passenger flow simulation to efficiently generate high-quality solutions. Finally, we conduct a series of numerical experiments using data from Guangzhou Metro Line 9 to demonstrate the effectiveness of our proposed approach. The results show that the proposed destination-to-gate assignment strategy effectively alleviates congestion-related risks across all stations and promotes spatial equity in passenger travel, even under varying levels of passenger compliance, demand, and train delays. It can thus be recommended as a self-organizing and easily implementable passenger flow control method.