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DSTGCS: an intelligent dynamic spatial–temporal graph convolutional system for traffic flow prediction in ITS

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Abstract

Accurate traffic prediction is indispensable for relieving traffic congestion and people’s daily trips. Nevertheless, accurate traffic flow prediction is still challenging due to the traffic network’s complex and dynamic spatial and temporal dependencies. Most existing methods usually ignore the dynamicity of spatial dependencies or have limitations, as using the self-attention mechanism for capturing dynamic spatial dependencies is computation forbidden in large networks. In addition, there are both short- and long-range dynamic temporal dependencies, which are not well captured. To overcome these limitations, we propose an intelligent dynamic spatial and temporal graph convolutional system for traffic flow prediction. First, we propose a dynamic spatial block to capture the complex and dynamic spatial dependencies, which is computation-friendly. Next, we propose a dynamic temporal block to capture the complex and dynamic temporal dependencies, which well balances the short- and long-range dynamic temporal dependencies. We validate and analyze the performance of the proposed method through extensive experiments on two traffic datasets. Analysis of results demonstrates that our proposed model has better prediction performance than the state-of-art baselines. Compared with the best contrast methods, the proposed method improves by 2.28% and 8.01% in terms of the mean absolute error on PEMS04 and PEMS08 datasets.

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Acknowledgements

The work was supported by the National Natural Science Foundation of China, under Grant Nos. 61976087 and 62072170.

Funding

The work is supported by the National Natural Science Foundation of China under Grants No. 61976087 and 62072170, the Science and Technology Project of the Department of Communications of Hunan Provincial under Grant 202101, the Key Research and Development Program of Hunan Province under Grant 2022GK2015, and the Open Research Fund of Hunan Provincial Key Laboratory of Network Investigational Technology under grant 2020WLZC001.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, China

    Na Hu & Wei Liang

  2. Hunan Key Laboratory for Service Computing and Novel Software Technology, Xiangtan, China

    Na Hu & Wei Liang

  3. College of Computer Science and Electronic Engineering, Hunan University, Changsha, China

    Dafang Zhang & Wei Liang

  4. Dipartimento di Informatica, Università degli Studi di Salerno, Fisciano, Italy

    Arcangelo Castiglione

  5. Dept. of Computer Science and Information Engineering, Providence University, Taichung, Taiwan

    Kuan-Ching Li

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  1. Na Hu
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  2. Dafang Zhang
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  4. Kuan-Ching Li
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  5. Arcangelo Castiglione
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Contributions

All authors contributed to the investigation’s conception and design. NH, DZ, and WL performed material preparation, data collection, and analysis. The manuscript’s validation, supervision, and writing were managed by K-CL, and software and writing by AC. All authors read and approved the final manuscript.

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Correspondence to Kuan-Ching Li.

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Hu, N., Zhang, D., Liang, W. et al. DSTGCS: an intelligent dynamic spatial–temporal graph convolutional system for traffic flow prediction in ITS. Soft Comput 28, 6909–6922 (2024). https://doi.org/10.1007/s00500-023-09553-3

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