Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 40, 2023 No. 3
766-770

Multi-head attention spatio-temporal convolutional graph network for traffic flow prediction

Xia Ying
Shi Zhiqi
School of Computer Science & Technology, Chongqing University of Posts & Telecommunications, Chongqing 400065, China
DOI: 10.19734/j.issn.1001-3695.2022.07.0362

Abstract

In order to fully obtain the complex dynamic spatio-temporal correlation hidden in the traffic flow data and improve the accuracy of traffic flow prediction, this paper proposed a multi-head attention spatiotemporal convolutional graph network model MASCGN. Firstly, by using the multi-head attention mechanism to automatically assign attention weights to the traffic sensor nodes in the road network, so as to realize the adaptive matching of the weights of different neighbor nodes and fully obtained spatial correlation. Secondly, it used the spatio-temporal convolutional network with gating and attention mechanism to fully extract the time series correlation, and used the residual block structure to realize the connection between the spatiotemporal convolutional layers, which made the model more generalizable. Finally, it extracted the series data of weekly correlation, daily correlation, and neighboring time, entered three parallel spatio-temporal components to dig deep into the temporal correlation between different time windows, and obtained the final traffic flow prediction result through the full connection layer. It conducted traffic flow prediction experiments for 15 min, 30 min, 45 min and 60 min using highway traffic datasets PEMSO4 and PEMSO8. Experimental results show that the MASCGN model has superior modeling capabilities compared to existing baseline models for both short-term and long-term future traffic forecasting tasks.

Foundation Support

国家自然科学基金资助项目(41971365)
重庆市教委重点合作项目(HZ2021008)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2022.07.0362
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 3
Section: Algorithm Research & Explore
Pages: 766-770
Serial Number: 1001-3695(2023)03-019-0766-05

Publish History

[2022-10-12] Accepted Paper
[2023-03-05] Printed Article

Cite This Article

夏英, 石栀琦. 面向交通流量预测的多头注意力时空卷积图网络模型 [J]. 计算机应用研究, 2023, 40 (3): 766-770. (Xia Ying, Shi Zhiqi. Multi-head attention spatio-temporal convolutional graph network for traffic flow prediction [J]. Application Research of Computers, 2023, 40 (3): 766-770. )

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  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    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.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

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