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A spatio-temporal grammar graph attention network with adaptive edge information for traffic flow prediction

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Abstract

Traffic flow prediction is an essential part of the intelligent traffic management system, which can help managers plan and maintain traffic order and individuals choose better travel routes. Due to the complex spatio-temporal correlation of large-scale transportation networks, it is challenging to build accurate and efficient prediction models. To address this issue, this paper proposes a spatio-temporal grammar graph attention network with adaptive edge information for traffic flow prediction. The external structure of the prediction model uses a grammar graph structure based on three grammar rules to capture the interactive relationship between various traffic parameters. The internal structure uses a graph attention network with adaptive edge information for synchronous extraction of the spatio-temporal dependence of historical traffic information. The two real data sets simulation results show that the model’s prediction accuracy is better than the existing prediction methods in different scale traffic networks.

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Data will be available on reasonable request.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China under grant 61973265.

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Authors and Affiliations

  1. Engineering Research Center of the Ministry of Education for Intelligent Control System and Intelligent Equipment, Yanshan University, 066004, Qinhuangdao, China

    Zhao Zhang & **aohong Jiao

  2. School of Electrical Engineering, Yanshan University, 066004, Qinhuangdao, China

    Zhao Zhang & **aohong Jiao

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  1. Zhao Zhang
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  2. **aohong Jiao
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Correspondence to **aohong Jiao.

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Zhang, Z., Jiao, X. A spatio-temporal grammar graph attention network with adaptive edge information for traffic flow prediction. Appl Intell 53, 28787–28803 (2023). https://doi.org/10.1007/s10489-023-05020-6

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