Multi-Frequency Spatial-Temporal Graph Neural Network for Short-Term Metro OD Demand Prediction during Public Health Emergencies

Short-term metro OD demand prediction during public health emergencies is a crucial task for the effective management and operation of metro systems. However, such emergencies tend to cause significant fluctuations in OD demand, making accurate prediction particularly challenging. To tackle this problem, this paper proposes a Multi-Frequency Spatial-Temporal Graph Neural Network (MFST-GNN) to accurately predict the metro OD demand during public health emergencies. Specifically, multiple OD demand patterns, including real-time, daily, and weekly OD demand are leveraged to extract the periodicity spatial-temporal features of OD demand. A novel multi-frequency temporal feature extraction module is developed to capture the periodic temporal features, while an adaptive spatial feature extraction module is introduced to learn the complex hidden spatial features. Moreover, event-related information is …

Recommended citation: J Zhang, S Zhang, H Zhao, Y Yang, M Liang*. (2025). Multi-Frequency Spatial-Temporal Graph Neural Network for Short-Term Metro OD Demand Prediction during Public Health Emergencies. Transportation. https://link.springer.com/article/10.1007/s11116-025-10582-0