Multi‐task graph‐based model for metro flow prediction under dynamic urban conditions

作者：Lu‐Tao Zhao, Linmu Zou, Zijia Wang, Taoran Song, Paul Schonfeld, Feng Chen, Rui Li, Pengcheng Li · 发表于：Computer-Aided Civil and Infrastructure Engineering · 年份：2025 · DOI：10.1111/mice.13505 · 被引用次数：6 · 研究领域：Traffic Prediction and Management Techniques、Human Mobility and Location-Based Analysis、Transportation Planning and Optimization

Accurately predicting metro commuter flows under changing urban conditions is essential for guiding infrastructure investments and service planning. However, existing methods show limited adaptability to evolving urban conditions. To address this, we propose an adaptive graph sharing embedding cascade interaction network (AGSECIN), which establishes a dynamic mapping relationship between changing urban conditions and commuter flows, enabling accurate predictions of metro inflows, outflows, and origin-destination (OD) flows simultaneously. A graph attention network is built on the long-term graph to capture the spatiotemporal evolving patterns of urban conditions. Then, an adaptive supply–demand sharing embedding network is designed to model the interaction between origin supply and destination demand. Finally, an adaptive feature interaction layer is developed to uncover the complex high-order relations among passenger flows and urban conditions. Experimental results on real-world Beijing datasets demonstrate the superior performance of AGSECIN, compared to contemporary models. Ablation experiments confirm the robustness of our model.