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To improve the efficiency of traffic information prediction, prevent traffic congestion, and reduce navigation conflicts, a mid-to-long-term ship traffic prediction method based on a spatiotemporal diffusion attention network is proposed. First, a method for constructing a maritime network is proposed, addressing the different structural characteristics of waterways and road networks. Next, a spatiotemporal diffusion attention network is built to enhance prediction accuracy. Then, using AIS data from the Huangpu River region as an example, a comparative analysis is conducted with a baseline model under the LibCity traffic prediction framework. Finally, the prediction results are visualized and analyzed. Experimental results show that the proposed model improves the evaluation metrics (MAE, MAPE, RMSE, \( R^2 \) ) compared to SOTA, with an improvement range of 1.6%–14.9%. The method surpasses the comparison models, thus validating its feasibility and effectiveness.

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Spatio-Temporal Diffusion Attention Networks for Vessel Flow Prediction

  • 会议论文

  • 发表日期：

    2026年

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  • Yuanyuan Pang,
  • Yong Li,
  • Qiang Mei,
  • Peng Wang

摘要

To improve the efficiency of traffic information prediction, prevent traffic congestion, and reduce navigation conflicts, a mid-to-long-term ship traffic prediction method based on a spatiotemporal diffusion attention network is proposed. First, a method for constructing a maritime network is proposed, addressing the different structural characteristics of waterways and road networks. Next, a spatiotemporal diffusion attention network is built to enhance prediction accuracy. Then, using AIS data from the Huangpu River region as an example, a comparative analysis is conducted with a baseline model under the LibCity traffic prediction framework. Finally, the prediction results are visualized and analyzed. Experimental results show that the proposed model improves the evaluation metrics (MAE, MAPE, RMSE, \( R^2 \) ) compared to SOTA, with an improvement range of 1.6%–14.9%. The method surpasses the comparison models, thus validating its feasibility and effectiveness.