A Pilot Study for the Service Level of the Metro Station Renovation Project Based on Passenger Detection and Simulation Techniques

With the rapid expansion of urban rail transit networks, the existing metro stations on older lines require renovation and upgrades when integrated with new line stations to accommodate additional transfer functionalities. This poses new challenges for the station’s spatial layout and passenger flow management. This study proposed a comprehensive technical system that leverages YOLO (you only look once) object detection technology in the operation of existing stations, coupled with AnyLogic simulation, to assess the impact of new line stations on passenger flow within old-line stations. The system was designed to predict congestion levels at old-line stations after they were upgraded to transfer stations. Passenger flow dynamics can be accurately monitored by collecting and analysing real-time data using YOLOv8 at the key nodes of existing stations. This approach can help us analyse the effective reference indicators to choose the best congestion mitigation plan. The simulation results provide a scientific basis for predicting and alleviating congestion at key nodes during the station renovation process, offering valuable references for ensuring passenger safety and enhancing the station throughput capacity.