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Light attention-based neural networks for traffic flow prediction

Author

Listed:
  • Li, Yong
  • Wang, Jiajun
  • Kang, Liujiang

Abstract

Spatial–temporal traffic patterns in transportation significantly influence the design of prediction models, which require both high accuracy and computational efficiency. This paper introduces the Light Attention-based Spatial-Temporal Neural Networks (Light-ASTNN), a lightweight traffic prediction model designed for higher prediction accuracy. The model integrates network topology information from a transportation network into a spatial attention to enhance the attention mechanism’s capacity. The effectiveness of the proposed model is validated through comparable experiments with a previous model, using 5 real-world traffic graph network-based datasets. The experimental results show that the proposed model can achieve a better performance in both the accuracy and computational efficiency, despite the fewer parameters. Furthermore, the experiments further highlight the critical role of network topology information in computing spatial correlations using the attention mechanism.

Suggested Citation

  • Li, Yong & Wang, Jiajun & Kang, Liujiang, 2025. "Light attention-based neural networks for traffic flow prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 673(C).
    • Handle: RePEc:eee:phsmap:v:673:y:2025:i:c:s0378437125003176
    DOI: 10.1016/j.physa.2025.130665
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    References listed on IDEAS

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    1. Zheng, Yan & Wang, Shengyou & Dong, Chunjiao & Li, Wenquan & Zheng, Wen & Yu, Jingcai, 2022. "Urban road traffic flow prediction: A graph convolutional network embedded with wavelet decomposition and attention mechanism," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 608(P1).
    2. Yang, Di & Li, Hong & Wang, Peng & Yuan, Lihong, 2024. "Multistep traffic speed prediction: A sequence-to-sequence spatio-temporal attention model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 638(C).
    3. Zhang, Jie & Song, Chunyue & Cao, Shan & Zhang, Chun, 2023. "FDST-GCN: A Fundamental Diagram based Spatiotemporal Graph Convolutional Network for expressway traffic forecasting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).
    4. Feng, Huifang & Jiang, Xintong, 2022. "Multi-step ahead traffic speed prediction based on gated temporal graph convolution network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 606(C).
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    Cited by:

    1. Cao, Jiayi & Chen, Jianzhong, 2025. "DDMGPN: A derivative-driven multi-graph propagation network with traffic knowledge graph for traffic flow prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 680(C).
    2. Yang, Xiaoxia & Wan, Jiahui & Li, Yongxing & Xie, Chuan-Zhi (Thomas) & Zhang, Botao, 2025. "A knowledge-data dual-driven framework for intelligent flood evacuation in subway stations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 678(C).

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