Optimization Model for Passenger Flow Control and Service Capacity Allocation in Subway Station Pedestrian Facility Networks

During peak hours, the large-scale and spatiotemporal imbalance of passenger flow in subway stations results in passenger crowding, queuing issues, and uneven utilization of facility capacities. These problems not only decrease the overall throughput efficiency of the station but also increase safety risks related to large passenger gatherings. This research constructed a pedestrian facility network for subway station access and egress by defining minimum capacity control units of node facilities (including station entrances/exits, fare gates, security check machines, and staircases/escalators) as network nodes and the connecting channels among these nodes were assumed as edges. With the optimization objectives of minimizing both the average walking time of passengers in the pedestrian facility network and the risk of passenger flow aggregation at nodes, an integrated optimization model for passenger flow control and service capacity configuration in the pedestrian facility network of subway stations is established. The ε-constraint method is employed to transform it into a single-objective linear integer programming model, which is then directly solved using the Gurobi optimizer version 11.0. The following conclusion were drawn form a case study on the National Convention and Exhibition Center Station of Shanghai Metro: compared with pre-optimization conditions, the optimized solution reduced the average walking time of access/egress passengers during peak hours by 11%, decreased the number of nodes with queue overflow by 76%, lowered node-level crowding risks by 45%, and reduced facility supply–demand balance standard deviation by 22.8%. Compared to single-objective optimization approaches, the proposed method only increased the average walking time by 8% while decreasing the number of overflow-prone nodes by 60% and crowding risk by 26.1%. These findings provided scientific support for the formulation of crowd management strategies and optimization of operational control in subway stations under heavy passenger flow conditions.