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Spatiotemporal synchronous dynamic graph attention network for traffic flow forecasting

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Abstract

Traffic flow forecasting (TFF) is crucial for effective urban planning and traffic management. Most modeling approaches in TFF ignore the dynamic characteristics of the transportation network topology, which results in an inability to accurately capture the hidden spatiotemporal correlations. To this end, we investigate a Spatiotemporal Synchronous Dynamic Graph Attention Network (STS-DGAT) for TFF. STS-DGAT is composed of three parts: the dynamic feature enhancement (DFE) module, the spatiotemporal coupling (STC) module, and the temporal position embedding (TPE) module. Specifically, we discover the impact of traffic data features on TFF based on the DFE module, assign dynamic weights of various features for time steps, and adjust the intrinsic relevance of traffic data. Then, we propose the STC module to characterize the complex coupling relationships of road network nodes in spatial and temporal dimensions and the dynamic intrinsic interactions of spatiotemporal correlations. The STC module comprises a dynamic graph attention network (DGAT) and an adaptive gated temporal convolutional network (AGTCN). Deep characterization for the dynamic topology of road networks is mined by DGAT, which captures real-time dynamic spatial correlations, and hidden features in the nonlinear temporal dimension are extracted by AGTCN to learn long-term temporal dependency. Finally, we put forward a TPE module to incorporate temporal position information into spatiotemporal relationships and adaptively learn hidden features of individual nodes to understand spatiotemporal variation features effectively. Experimental results from four real-world datasets demonstrate that the STS-DGAT model outperforms other baseline models.

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Data availability

The datasets generated during and analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

This work described in this paper was supported in part by the National Natural Science Foundation of China (Grant nos. 62162012, 62173278, 62072061, and 62341115), the High-Level Innovative Talent Project of Guizhou Province (Grant no. QKHPTRC-GCC2023027), the Natural Science Research Project of Department of Education of Guizhou Province (Grant nos. QJJ2022015, QJJ2022047, QJJ2023012, and QJJ2023061), the Scientific Research Platform Project of Guizhou Minzu University (Grant no. GZMUSYS202104), and the Natural Science Foundation of Guizhou Minzu University (Grant no. GZMUZK2022YB19).

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Authors and Affiliations

  1. College of Data Science and Information Engineering, Guizhou Minzu University, Guiyang, 550025, China

    Dawen Xia, Zhan Lin & Yan Chen

  2. Department of Automotive Engineering, Guizhou Traffic Technician and Transportation College, Guiyang, 550008, China

    Yang Hu

  3. College of Computer Science, Chongqing University, Chongqing, 400044, China

    Yantao Li

  4. College of Electronic and Information Engineering, Southwest University, Chongqing, 400715, China

    Huaqing Li

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  1. Dawen Xia
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Correspondence to Huaqing Li.

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Xia, D., Lin, Z., Chen, Y. et al. Spatiotemporal synchronous dynamic graph attention network for traffic flow forecasting. Neural Comput & Applic (2024). https://doi.org/10.1007/s00521-024-09675-1

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