Passenger flow distribution forecasting at integrated transport hub via group evolution mechanism and multimodal data
Zhicheng Dai (Beijing Jiaotong University, Eindhoven University of Technology)
Dewei Li (Beijing Jiaotong University)
Soora Rasouli (Eindhoven University of Technology)
Yan Feng (TU Delft - Traffic Systems Engineering)
Hua Li (China Railway Shanghai Group, Co., Ltd.)
Linhan Zou (Beijing Jiaotong University)
Ruonan Zhang (Beijing Jiaotong University)
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Abstract
Integrated transport hubs require reliable, fine-grained forecasts of crowd distribution to safeguard operations and sustainable urban mobility. We present Group Evolution Mechanism Embedded Network (GEME-Net), a passenger flow distribution forecasting architecture that fuses multimodal data, including video-derived counts, digital twin-based mobility chains, and railway/metro operations information, via multi-graph spatial representations and event-aware temporal modules, with a distilled lightweight student model for deployment. In a real-world case at Shanghai Hongqiao, GEME-Net consistently outperforms statistical, convolutional, recurrent, graph-based and Transformer baselines across MAE, RMSE and WMAPE, while retaining inference latency compatible with near-real-time use. Ablations indicate that schedule encoding and event-driven frequency enhancement, together with learned long-range and community graphs, are principal contributors to accuracy. By coupling operational signals with spatial semantics, our approach improves hub-scale situation awareness and short-horizon decision support, offering a practical route to resilient crowd management without asserting broader societal or policy impacts.
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