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<p>Short-term metro OD demand prediction during public health emergencies is a crucial task for the effective management and operation of metro systems. However, &#xa0;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 collected and integrated into the OD features to study the impact of events on OD demand. The effectiveness of the proposed model is validated by a large-scale real-world metro OD dataset, with comparative analysis against benchmark prediction models. Results demonstrate its superior performance and practical application potential.</p>

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Multi-frequency spatial-temporal graph neural network for short-term metro OD demand prediction during public health emergencies

  • 期刊论文

  • 发表日期：

    2025年2月11日

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  • Jinlei Zhang,
  • Shuxin Zhang,
  • Haobo Zhao,
  • Yongjie Yang,
  • Maohan Liang

摘要

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 collected and integrated into the OD features to study the impact of events on OD demand. The effectiveness of the proposed model is validated by a large-scale real-world metro OD dataset, with comparative analysis against benchmark prediction models. Results demonstrate its superior performance and practical application potential.