App. Res. of Comp. Accepted Paper
Vol. 41, 2024 No. 10

Traffic flow prediction method based on spatial temporal position attention graph neural networks

He Ting
Zhou Yanqiu
Xin Chunhua
Dept. of Computer Technology & Information Management, Inner Mongolia Agricultural University, Tuyou Banner, Baotou Nei Mongol Zizhiqu 010010, China
DOI: 10.19734/j.issn.1001-3695.2024.01.0026

Abstract

To address the challenge of constructing spatial and temporal dependencies in existing traffic flow prediction methods, we proposed a new method called Spatial Temporal Position Attention Graph Neural Networks (ST-PAGNN) , which utilizes spatiotemporal location attention. Firstly, the graph neural network contains a location attention mechanism, which can better capture the spatial dependence of traffic nodes in the urban road network. Then, we use a gated recurrent neural network with Trend Adaptive Transformer (Trendformer) to capture the local and global information of the traffic flow sequence in the time dimension. Finally, we use the improved grid search optimization method to optimize the introduced parameters of the model, obtaining the global optimal solution with high time efficiency. The experimental results show that in the dataset PEMS-BAY, the evaluation indexes RMSE, MAE and MAPE of the T-PAGNN method are 1.37, 2.57, 2.67%, 1.55, 3.64, 3.37%, 1.97, 4.37 and 4.43%, respectively, when the prediction step size is 15 min, 30 min and 60 min, respectively. In the dataset METR-LA, when the prediction step sizes were 15 min, 30 min and 60 min, the evaluation indexes RMSE, MAE and MAPE of the ST-PAGNN method were 2.73, 5.16, 7.13%, 2.99, 5.97, 7.86%, 3.53, 7.16 and 9.96%, respectively. The results show that the proposed ST-PAGNN method is higher than the existing models in the evaluation indexes under different granularities, which illustrates the effectiveness and superiority of ST-PAGNN in solving traffic prediction problems.

Foundation Support

国家自然科学基金资助项目(31960361)
内蒙古自治区科技计划资助项目(2020GG0033)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.01.0026
Publish at: Application Research of Computers Accepted Paper, Vol. 41, 2024 No. 10

Publish History

[2024-04-18] Accepted Paper

Cite This Article

何婷, 周艳秋, 辛春花. 基于时空位置关注图神经网络的交通流预测方法 [J]. 计算机应用研究, 2024, 41 (10). (2024-07-12). https://doi.org/10.19734/j.issn.1001-3695.2024.01.0026. (He Ting, Zhou Yanqiu, Xin Chunhua. Traffic flow prediction method based on spatial temporal position attention graph neural networks [J]. Application Research of Computers, 2024, 41 (10). (2024-07-12). https://doi.org/10.19734/j.issn.1001-3695.2024.01.0026. )

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  • Application Research of Computers Monthly Journal
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    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

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