We propose a passenger flow detection method for dense areas of subway stations to address the current situation that existing pedestrian detection models cannot meet the real-time performance requirements in subway applications and lack validation in multiple subway scenarios. First, we designed the MPDNet model, which uses PVT-small to extract features and an improved feature pyramid network (FPN) for upsampling using the adaptively spatial feature fusion (ASFF) algorithm to retain more local information in the output of the FPN. Second, to better evaluate the performance of models in the metro, we collected subway surveillance video data and proposed the MetroStation dataset. Finally, we trained and evaluated the performance of the MPDNet model on the MetroStation dataset. We compare our method with several common object detection models on the MetroStation dataset, using mAP and frames per second (FPS) to verify its accuracy. The experiments on the MetroStation dataset demonstrated that the MPDNet performed well and satisfied inference speed requirements in metro passenger flow detection.

Metro intelligent system produces massive passenger flow and traffic data every day, among which route, station, and operation data are important for optimizing the train operation scheme. We collect passenger flow information of Shenzhen metro, analyze the passenger flow pattern and its distribution characteristics based on the data warehouse of the Hadoop platform, and optimize the train operation scheme in this paper. Using dynamic passenger flow data, an optimization model with train departure and dwell time as decision variables and passenger waiting time, passenger ride time, train full load ratio, and train operation balance as objectives is developed. An improved parallel genetic algorithm (GA) incorporating a simulated annealing algorithm (SAA) and an optimal individual retention strategy is used to find the optimal result. To verify the usefulness of the method, simulation experiments are conducted on the optimization model and method using the real passenger flow and train operation data of Shenzhen metro, and the simulation results are compared with the original plan.