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Multi-dynamic residual graph convolutional network with global feature enhancement for traffic flow prediction

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Abstract

The key to achieving an accurate and reliable traffic flow prediction lies in modeling the complex and dynamic correlations among sensors. However, existing studies ignore the fact that such correlations are influenced by multiple dynamic factors and the original sequence features of the traffic data, which limits the deep modeling of such correlations and leads to a biased understanding of such correlations. The extraction strategies for global features are less developed, which has degraded the reliability of the predictions. In this study, a novel multi-dynamic residual graph convolutional network with global feature enhancement is proposed to solve these problems and achieve an accurate and reliable traffic flow prediction. First, multiple graph generators are proposed, which fully preserve the original sequence features of the traffic data and enable layered depth-wise modeling of the dynamic correlations among sensors through a residual mechanism. Second, an output module is proposed to explore extraction strategies for global features, by employing a residual mechanism and parameter sharing strategy to maintain the consistency of the global features. Finally, a new layered network architecture is proposed, which not only leverages the advantages of both static and dynamic graphs, but also captures the spatiotemporal dependencies among sensors. The superiority of the proposed model has been verified through extensive experiments on two real-world datasets.

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Data availability statement

The datasets generated and/or analyzed during the current study are available from the website (https://pems.dot.ca.gov).

Notes

  1. https://pems.dot.ca.gov/

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Acknowledgements

This work has been supported by Zhejiang Province Key R & D Program Projects of China (No.2022C03166, No.2024C01034), and Zhejiang Provincial Science and Technology Plan Project of China (No.2023R5213).

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Authors and Affiliations

  1. School of Information Technology, Zhejiang Yuying College of Vocational Technology, Hangzhou, 310018, China

    Xiangdong Li

  2. School of Information Technology and Artificial Intelligence, Zhejiang University of Finance and Economics, Hangzhou, 310018, China

    Xiang Yin, Weishu Liu & Shuai Zhang

  3. State Key Laboratory of Ocean Engineering, School of Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China

    Xiang Yin

  4. Library, Zhejiang University of Finance and Economics, Hangzhou, 310018, China

    Xiaoling Huang

  5. College of Information Engineering, China Jiliang University, Hangzhou, 310018, China

    Dongping Zhang

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  1. Xiangdong Li
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Contributions

Xiangdong Li: Conceptualization, Formal analysis, Writing- Reviewing & Editing, Supervision. Xiang Yin: Methodology, Formal analysis, Writing- Original draft, Software, Validation. Xiaoling Huang: Conceptualization, Writing- Reviewing & Editing, Supervision. Weishu Liu: Conceptualization, Writing- Reviewing & Editing. Shuai Zhang: Writing- Reviewing & Editing, Supervision, Funding Acquisition. Dongping Zhang: Writing- Reviewing & Editing.

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Correspondence to Xiang Yin.

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Li, X., Yin, X., Huang, X. et al. Multi-dynamic residual graph convolutional network with global feature enhancement for traffic flow prediction. Int. J. Mach. Learn. & Cyber. 16, 873–889 (2025). https://doi.org/10.1007/s13042-024-02307-z

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