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Attention-based spatial–temporal adaptive dual-graph convolutional network for traffic flow forecasting

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Abstract

Accurate traffic flow forecasting (TFF) is a prerequisite for urban traffic control and guidance, which has become the key to avoiding traffic congestion and improving traffic management in intelligent transportation systems. To precisely characterize the spatial structure of road networks and discover temporal and spatial characteristics, we propose an attention-based spatial–temporal adaptive dual-graph convolutional network (ASTA-DGCN) for TFF in this paper. Specifically, we employ a spatial–temporal attention module to explore the hidden temporal correlation information of traffic data and the implicit influence of weights among road network nodes and to further capture the dynamic influence of different spatial–temporal positions on the current spatial–temporal position. Then, we utilize an adaptive graph modeling module to automatically extract the one-way relationship between variables and integrate external knowledge into the module. The FastDTW algorithm is exploited to measure the similarity of road network nodes, and the non-Euclidean pairwise association between regions is encoded into graphs to discover the hidden temporal pattern similarity effectively. Furthermore, temporal and spatial correlations are explicitly modeled using dual-graph convolution and sequential convolution based on the obtained graphs to mine the spatial–temporal patterns in dynamic traffic flow, and the final prediction result is produced based on the weighted fusion of the output values of the recent, daily, and weekly components. Finally, the ASTA-DGCN algorithm is successfully applied to TFF on two real-world traffic datasets. The experimental results indicate that our ASTA-DGCN algorithm outperforms ARIMA, VAR, FNN, GCN, GAT, GWNet, STGCN, ASTGCN, and STSGCN.

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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, and 62072061), the Science and Technology Support Program of Guizhou Province, China (Grant no. QKHZC2021YB531), the Natural Science Research Project of Department of Education of Guizhou Province, China (Grant nos. QJJ2022015 and QJJ2022047), and the Scientific Research Platform Project of Guizhou Minzu University, China (Grant no. GZMUSYS[2021]04).

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Authors and Affiliations

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

    Dawen Xia, Bingqi Shen & Jian Geng

  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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  2. Bingqi Shen
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Correspondence to Huaqing Li.

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Xia, D., Shen, B., Geng, J. et al. Attention-based spatial–temporal adaptive dual-graph convolutional network for traffic flow forecasting. Neural Comput & Applic 35, 17217–17231 (2023). https://doi.org/10.1007/s00521-023-08582-1

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