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An improved dynamic Chebyshev graph convolution network for traffic flow prediction with spatial-temporal attention

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Abstract

Accurate traffic flow prediction plays a significant role in urban traffic management, including traffic congestion control and public travel route planning. Recently, several approaches have been put forward to learn the patterns from historical traffic data. However, there exist some limitations resulting from the use of the static learning method to explore the dynamical characteristics of the road network. Besides, the dynamic global temporal and spatial properties are not considered in these models. These drawbacks lead to a low prediction performance and make applying to a more extensive road network challenging. To address these issues, from the inspiration of Chebyshev polynomial, we proposed an improved dynamic Chebyshev graph convolution neural network model called iDCGCN. In the proposed approach, a novel updating method for the Laplacian matrix, which approximately constructs features from different period data, is proposed based on the attention mechanism. In addition, a novel feature construction method is proposed to integrate long-short temporal and local-global spatial features for complex traffic flow representation. Experimental results have shown that iDCGCN outperforms the state-of-the-art GCN-based methods on four real-world highway traffic datasets.

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Data availability

The original highway datasets are derived from [22], and further raw data used in this work can be obtained from the corresponding author upon request.

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Acknowledgments

This work is supported in part by projects of the National Natural Science Foundation of China (41971340, 41471333, 61304199) and Fujian Provincial Department of Science and Technology (2021Y4019, 2020D002, 2020 L3014, 2019I0019).

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Authors and Affiliations

  1. Fujian University of Technology, Fuzhou, China

    Lyuchao Liao, Zhiyuan Hu, Yuxin Zheng, Fumin Zou & Maolin Zhang

  2. School of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing, China

    Shuoben Bi

  3. Fujian Expressway Group Co., Ltd, Fuzhou, China

    Huai Qiu

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  1. Lyuchao Liao
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  2. Zhiyuan Hu
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  3. Yuxin Zheng
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  4. Shuoben Bi
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  5. Fumin Zou
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  6. Huai Qiu
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  7. Maolin Zhang
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Correspondence to Zhiyuan Hu.

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Liao, L., Hu, Z., Zheng, Y. et al. An improved dynamic Chebyshev graph convolution network for traffic flow prediction with spatial-temporal attention. Appl Intell 52, 16104–16116 (2022). https://doi.org/10.1007/s10489-021-03022-w

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