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An Attention and Wavelet Based Spatial-Temporal Graph Neural Network for Traffic Flow and Speed Prediction

Author

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  • Shihao Zhao

    (School of Computer Science and Mathematics, Fujian University of Technology, Fuzhou 350118, China)

  • Shuli Xing

    (School of Computer Science and Mathematics, Fujian University of Technology, Fuzhou 350118, China)

  • Guojun Mao

    (School of Computer Science and Mathematics, Fujian University of Technology, Fuzhou 350118, China
    Fujian Provincial Key Lab of Big Data Mining and Applications, Fuzhou 350118, China)

Abstract

Traffic flow prediction is essential to the intelligent transportation system (ITS). However, due to the complex spatial-temporal dependence of traffic flow data, it is insufficient in the extraction of local and global spatial-temporal correlations for the previous process on road network and traffic flow modeling. This paper proposes an attention and wavelet-based spatial-temporal graph neural network for traffic flow and speed prediction (STAGWNN). It integrated attention and graph wavelet neural networks to capture local and global spatial information. Meanwhile, we stacked a gated temporal convolutional network (gated TCN) with a temporal attention mechanism to extract the time series information. The experiment was carried out on real public transportation datasets: PEMS-BAY and PEMSD7(M). The comparison results showed that our proposed model outperformed baseline networks on these datasets, which indicated that STAGWNN could better capture the spatial-temporal correlation information.

Suggested Citation

  • Shihao Zhao & Shuli Xing & Guojun Mao, 2022. "An Attention and Wavelet Based Spatial-Temporal Graph Neural Network for Traffic Flow and Speed Prediction," Mathematics, MDPI, vol. 10(19), pages 1-15, September.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:19:p:3507-:d:925481
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    References listed on IDEAS

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    1. Cai, Lingru & Zhang, Zhanchang & Yang, Junjie & Yu, Yidan & Zhou, Teng & Qin, Jing, 2019. "A noise-immune Kalman filter for short-term traffic flow forecasting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 536(C).
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    Cited by:

    1. Ke Zhang & Yaming Guo, 2023. "Attention-Based Residual Dilated Network for Traffic Accident Prediction," Mathematics, MDPI, vol. 11(9), pages 1-15, April.
    2. Anton Agafonov & Alexander Yumaganov & Vladislav Myasnikov, 2023. "Cooperative Control for Signalized Intersections in Intelligent Connected Vehicle Environments," Mathematics, MDPI, vol. 11(6), pages 1-19, March.

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