OD-Enhanced Dynamic Spatial-Temporal Graph Convolutional Network for Metro Passenger Flow Prediction

Metro passenger flow prediction is crucial for efficient urban transportation planning and resource allocation. However, it faces two challenges. The first challenge is extracting the diverse passenger flow patterns at different stations, e.g., stations near residential areas and stations near commercial areas, while the second one is to model the complex dynamic spatial-temporal correlations caused by Origin-Destination (OD) flows. Existing studies often overlook the above two aspects, especially the impact of OD flows. In conclusion, we propose an OD-enhanced dynamic spatial-temporal graph convolutional network (DSTGCN) for metro passenger flow prediction. First, we propose a static spatial module to extract the flow patterns of different stations. Second, we utilize a dynamic spatial module to capture the dynamic spatial correlations between stations with OD matrices. Finally, we employ a multi-resolution temporal dependency module to learn the delayed temporal features. We also conduct experiments based on two real-world datasets in Shanghai and Hangzhou. The results show the superiority of our model compared to the state-of-the-art baselines.