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DyAdapTransformer: Dynamic Adaptive Spatial-Temporal Graph Transformer for Traffic Prediction

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Spatial Data and Intelligence (SpatialDI 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14619))

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Abstract

The transformer-based method is a popular choice for medium and long-term traffic prediction. However, it still suffers from some problems. The first is that spatial position embedding has poor interpretability. Additionally, the spatial-temporal correlation learning can struggle to reflect the actual complexity of traffic networks relationships. To address the above problems, we propose a traffic prediction framework for dynamic adaptive spatial-temporal graph transformer (DyAdapTransformer). Our method uses the method of random walk to embed the spatial position. The analyzability between transition probability and spatial position representation enhances the interpretability of the model. When learning spatial-temporal correlation, a method of dynamic adaptive graph attention network is proposed. We compared with our framework with four baselines on three datasets. The results show that DyAdapTransformer has a better predictive performance.

Hui Dong is currently pursuing the Ph.D. degree in the School of Management, Shijiazhuang Tiedao University.

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Acknowledgments

This work was supported in part by the Natural Science Foundation of Hebei Province under Grant F2021210005 and F2023407003; in part by the Outstanding Youth Foundation of Hebei Education Department under Grant BJ2021085; in part by the Postgraduate Innovation Foundation of Hebei under Grant CXZZBS2022117; in part by the Key Laboratory of Marine Dynamic Process and Resources and Environment Open Course of Hebei Province under Grant HBHY02.

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Authors and Affiliations

  1. School of Management, Shijiazhuang Tiedao University, Shijiazhuang, 050043, China

    Hui Dong

  2. School of Information Science and Technology, Shijiazhuang Tiedao University, Shijiazhuang, 050043, China

    Xiao Pan & Jing Sun

  3. Hebei Key Laboratory of Ocean Dynamics, Resources and Environments, Qinhuangdao, 066004, China

    Xiao Chen

  4. Department of Science and Technology, Shijiazhuang Tiedao University, Shijiazhuang, 050043, China

    Shuhai Wang

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  1. Hui Dong
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  2. Xiao Pan
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  3. Xiao Chen
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  4. Jing Sun
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  5. Shuhai Wang
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Corresponding author

Correspondence to Xiao Pan .

Editor information

Editors and Affiliations

  1. Renmin University of China, Beijing, China

    Xiaofeng Meng

  2. Nanjing Normal University, Nanjing, China

    Xueying Zhang

  3. Beijing University of Chemical Technology, Beijing, China

    Danhuai Guo

  4. Nanjing Normal University, Nanjing, China

    Di Hu

  5. Huazhong University of Science and Technology, Wuhan, China

    Bolong Zheng

  6. Hefei University of Technology, Hefei, China

    Chunju Zhang

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Dong, H., Pan, X., Chen, X., Sun, J., Wang, S. (2024). DyAdapTransformer: Dynamic Adaptive Spatial-Temporal Graph Transformer for Traffic Prediction. In: Meng, X., Zhang, X., Guo, D., Hu, D., Zheng, B., Zhang, C. (eds) Spatial Data and Intelligence. SpatialDI 2024. Lecture Notes in Computer Science, vol 14619. Springer, Singapore. https://doi.org/10.1007/978-981-97-2966-1_17

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  • DOI: https://doi.org/10.1007/978-981-97-2966-1_17

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-2965-4

  • Online ISBN: 978-981-97-2966-1

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