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Traffic Flow Prediction Based on Dynamic Graph Spatial-Temporal Neural Network

Author

Listed:
  • Ming Jiang

    (School of Internet Economics and Business, Fujian University of Technology, Fuzhou 350014, China)

  • Zhiwei Liu

    (School of Transportation, Fujian University of Technology, Fuzhou 350108, China)

Abstract

More accurate traffic prediction can further improve the efficiency of intelligent transportation systems. However, the complex spatiotemporal correlation issues in transportation networks pose great challenges. In the past, people have carried out a great deal of research to solve this problem. Most studies are based on graph neural networks to model traffic graphs and attempt to use fixed graph structures to obtain relationships between nodes. However, due to the time-varying spatial correlation of the transportation network, there is no stable node relationship. To address the above issues, we propose a new traffic prediction framework called the Dynamic Graph Spatial-Temporal Neural Network (DGSTN). Unlike other models that use predefined graphs, this model represents stable node relationships and time-varying node relationships by constructing static topology maps and dynamic information maps during the training and testing stages, to capture hidden node relationships and time-varying spatial correlations. In terms of network architecture, we designed multi-scale causal convolution and adaptive spatial self-attention mechanisms to capture temporal and spatial features, respectively, and assisted learning through static and dynamic graphs. The proposed framework has been tested on two real-world traffic datasets and can achieve state-of-the-art performance.

Suggested Citation

  • Ming Jiang & Zhiwei Liu, 2023. "Traffic Flow Prediction Based on Dynamic Graph Spatial-Temporal Neural Network," Mathematics, MDPI, vol. 11(11), pages 1-16, May.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:11:p:2528-:d:1160762
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    Citations

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    Cited by:

    1. Kai Zhang & Zixuan Chu & Jiping Xing & Honggang Zhang & Qixiu Cheng, 2023. "Urban Traffic Flow Congestion Prediction Based on a Data-Driven Model," Mathematics, MDPI, vol. 11(19), pages 1-20, September.
    2. Wenguang Chai & Yuexin Zheng & Lin Tian & Jing Qin & Teng Zhou, 2023. "GA-KELM: Genetic-Algorithm-Improved Kernel Extreme Learning Machine for Traffic Flow Forecasting," Mathematics, MDPI, vol. 11(16), pages 1-15, August.

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