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Spatial dynamic graph convolutional network for traffic flow forecasting

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Abstract

The complex traffic network spatial correlation and the characteristic of high nonlinear and dynamic traffic conditions in the time are the challenges to accurate traffic flow forecasting. Existing spatiotemporal models attempt to utilize the static graph to explore spatial dependency and employ RNN-based model to capture temporal dependency. However, the static graph fails to reflect the dynamic changeable correlation between each node. That is some nodes have a strong connection in a real traffic network, whereas a weak connection is in a static predefined graph. To overcome the above problems, we propose a spatial dynamic graph convolutional network (SDGCN) for traffic flow forecasting. With the support of an attention fusion network in graph learning, SDGCN generates the dynamic graph at each time step, which can model the changeable spatial correlation from traffic data. By embedding dynamic graph diffusion convolution into gated recurrent unit, our model can explore spatio-temporal dependency simultaneously. Moreover, to handle long sequence forecasting, ReZero transformer is utilized to detect the global temporal correlation capturing. The experiments are conducted on two public datasets. The experimental results demonstrate the superior performance of our network.

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Acknowledgements

The data that support the findings of this study are available from the corresponding author upon reasonable request. Huaying Li and Shumin Yang contribute equally. This work was supported by the National Natural Science Foundation of China (No. 61902232), the 2022 Guangdong Basic and Applied Basic Research Foundation (No. 2022A1515011590), the STU Incubation Project for the Research of Digital Humanities and New Liberal Arts (No. 2021DH-3), the 2020 Li Ka Shing Foundation Cross-Disciplinary Research Grant (No. 2020LKSFG05D), the Innovation School Project of Guangdong Province (No. 2017KCXTD015), and the Open Fund of Guangdong Provincial Key Laboratory of Infectious Diseases and Molecular Immunopathology (No. GDKL202212).

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Authors and Affiliations

  1. Department of Computer Science, College of Engineering, Shantou University, Shantou, China

    Huaying Li, Shumin Yang & Teng Zhou

  2. Center for Smart Health, School of Nursing, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hong Kong, Hong Kong

    Youyi Song

  3. School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, China

    Yu Luo

  4. Mechanical Engineering College, Xi’an Shiyou University, Xi’an, China

    Junchao Li

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  1. Huaying Li
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Correspondence to Teng Zhou.

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Li, H., Yang, S., Song, Y. et al. Spatial dynamic graph convolutional network for traffic flow forecasting. Appl Intell 53, 14986–14998 (2023). https://doi.org/10.1007/s10489-022-04271-z

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