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Traffic flow prediction based on graph convolutional networks with a parallel attention network and stacked gate recurrent units

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Abstract

Accurate traffic flow prediction is essential to address traffic issues and assist traffic managers make informed decisions in intelligent transportation systems. Extracting potential features from traffic data is challenging due to the complex topology of urban road networks and the time-varying traffic flow. To capture the global spatiotemporal characteristics of traffic flow, we propose a novel model based on graph convolutional networks with a parallel attention network and stacked gated recurrent units (PAGCN-SGRU). First, the parallel attention (PA) network enhances the feature representation of global traffic road nodes and road segments. Then, the graph convolutional networks (GCN) are designed to extract spatial characteristics. Next, the stacked gate recurrent units (SGRU) are employed to capture temporal features. Finally, PAGCN-SGRU discovers global spatiotemporal features for traffic flow prediction. The experimental results demonstrate that the accuracy of PAGCN-SGRU under the SZ-dataset is improved by 9.76\(\%\), 72.54\(\%\), 5.76\(\%\), 16.07\(\%\), 2.07\(\%\), 1.82\(\%\), 3.35\(\%\), and 6.59\(\%\), respectively, compared to that of HA, ARIMA, SVR, GCN, T-GCN, A3T-GCN, ST-GCN, and DCRNN. In the Los-dataset, the accuracy values increase by 7.11\(\%\), 8.94\(\%\), 6.66\(\%\), 7.77\(\%\), 3.43\(\%\), 2.45\(\%\), 2.28\(\%\), and 4.09\(\%\), respectively.

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Data Availability Statements

The datasets generated during and analysed 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 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 no. QJJ2022015), the Fundamental Research Funds for the Central Universities (Grant no. SWU-XDJH202312), 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 **a, Yuce Ao, **aoduo Wei, Yunsong Li & 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 **a
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**a, D., Ao, Y., Wei, X. et al. Traffic flow prediction based on graph convolutional networks with a parallel attention network and stacked gate recurrent units. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-19479-z

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