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Spatiotemporal Adaptive Fusion Graph Network for Short-Term Traffic Flow Forecasting

Author

Listed:
  • Shumin Yang

    (Department of Computer Science, Shantou University, Shantou 515000, China
    These authors contributed equally to this work.)

  • Huaying Li

    (Department of Computer Science, Shantou University, Shantou 515000, China
    These authors contributed equally to this work.)

  • Yu Luo

    (School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, China)

  • Junchao Li

    (Mechanical Engineering College, Xi’an Shiyou University, Xi’an 710312, China)

  • Youyi Song

    (Center for Smart Health, School of Nursing, The Hong Kong Polytechnic University, Hong Kong, China)

  • Teng Zhou

    (Department of Computer Science, Shantou University, Shantou 515000, China
    Key Laboratory of Intelligent Manufacturing Technology, Shantou University, Ministry of Education, Shantou 515000, China)

Abstract

Traffic flow forecasting is challenging for us to analyze intricate spatial–temporal dependencies and obtain incomplete information of spatial–temporal connection. Existing frameworks mostly construct spatial and temporal modeling based on a fixed graph structure and given time series. However, a fixed adjacency matrix is limited to learn effective spatial–temporal correlations of the network because it represents incomplete information for missing genuine relation. To solve the difficulty, we design a novel spatial–temporal adaptive fusion graph network (STFAGN) for traffic prediction. First, our model combines fusion convolution layers with a novel adaptive dependency matrix by end-to-end training to capture the hidden spatial-temporal dependency on the data to complete incomplete information. Second, STFAGN could, in parallel, acquire hidden spatial–temporal dependencies by a fusion operation and temporal trend by fast-DTW. Meanwhile, we use ReZero connection as a simple change of deep residual networks to facilitate deep signal propagation and faster converge. Lastly, we conduct comparative experiments on two public traffic network datasets, whose results demonstrate the superiority of our algorithm compared to state-of-the-art baseline types. Ablation experiments also prove the rationality of the framework of STFAGN.

Suggested Citation

  • Shumin Yang & Huaying Li & Yu Luo & Junchao Li & Youyi Song & Teng Zhou, 2022. "Spatiotemporal Adaptive Fusion Graph Network for Short-Term Traffic Flow Forecasting," Mathematics, MDPI, vol. 10(9), pages 1-12, May.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:9:p:1594-:d:810859
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    Citations

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    Cited by:

    1. Ismail Shah & Izhar Muhammad & Sajid Ali & Saira Ahmed & Mohammed M. A. Almazah & A. Y. Al-Rezami, 2022. "Forecasting Day-Ahead Traffic Flow Using Functional Time Series Approach," Mathematics, MDPI, vol. 10(22), pages 1-16, November.
    2. Zhihan Cui & Boyu Huang & Haowen Dou & Yan Cheng & Jitian Guan & Teng Zhou, 2022. "A Two-Stage Hybrid Extreme Learning Model for Short-Term Traffic Flow Forecasting," Mathematics, MDPI, vol. 10(12), pages 1-17, June.

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