An Optimization Method for Metro Passenger Flow Control by Tracing Congestion Sources

Single-station control measures cannot alleviate network-level congestion from the source. This study investigates a multi-station collaborative control method to address peak-hour congestion and safety hazards through tracing passenger flow sources in metro networks. This method identifies source stations of congestion through three dimensions. First, the stations with persistently high inbound volumes are selected to reduce the pressure entering the network. Second, the stations with significant transfer congestion are located, and their main source stations are determined via tracing passenger flow. Third, the bottleneck sections are traced to identify the initial source stations. A multi-dimensional source tracing model is constructed to determine which stations are require to control. Based on this logic, an optimization model is developed. This model minimizes three objectives: the number of stranded passengers, the average load factor, and the maximum platform occupancy. It links source control with transfer relief through dynamic adjustment of inbound volumes. The constraint conditions include interval capacity, platform capacity, and load factor. An improved non-dominated sorting genetic algorithm (NSGA-Ⅱ) is designed to solve the model. The algorithm incorporates constraint-aware genetic operators, a fusion of multi-objective fitness with constraint penalties, and problem-specific population initialization to enhance solution accuracy and efficiency. A case study is conducted on 111 stations across five lines of an urban metro network. After implementing the pro-posed control, the standard deviation of load factors decreases by 1.50% on Line 1 and by 2.69% on Line 2. This indicates a more balanced network passenger distribution. The number of high-risk spatiotemporal units decreases by 5.88%. The stranded passengers at key transfer stations decrease by 31.4%, effectively suppressing extreme crowding risks. The peak load factors in bottleneck sections decline by 4.6% to 6.9%, alleviating the congestion in these intervals. This method reduces the congestion risks at key stations during peak hours. It also improves the overall operational efficiency of metro networks.