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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 4
1041-1046

Traffic flow prediction model based on combining periodic graph convolution network and multi-head attention GRU

Zhong Linlan1
Zhang Anqin1,2
Tian Xiuxia1
1. School of Computer Science & Technology, Shanghai University of Electric Power, Shanghai 201306, China
2. Institute of Local Government Development, Shantou University, Shantou Guangdong515063, China
DOI: 10.19734/j.issn.1001-3695.2023.08.0362

Abstract

To capture the complex spatial-temporal dynamics and periodic patterns in traffic flow data, and reduce the cumulative error effects caused by unexpected road conditions, this paper proposed a traffic flow prediction model based on combining PGCN and MAGRU. Firstly, the spatial-temporal data fusion module constructed periodic graphs using the property of periodic similarity in traffic flow data, and added spatial and temporal encoding information into the sequence data. Then, in the spatial-temporal feature extraction module, graph convolutional network(GCN) submodule captured spatial features from the periodic feature graphs, MAGRU submodule captured temporal features from the sequence data. Finally, the gated fusion mechanism fused the features extracted by both modules. It conducted the experiment on two real traffic flow datasets. The indicate that compared to several recent baseline models, the model achieves average reduction of 5.4%, 22.8%, 10.3% in MAE, RMSE and MAPE, exhibits an average improvement of 11.6% in R2 accuracy metric, which confirms that the model can provide more accurate predictions and reduce cumulative error effects.

Foundation Support

广东省人文社会科学重点研究基地-汕头大学地方政府发展研究所开放基金课题(07422002)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.08.0362
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 4
Section: Algorithm Research & Explore
Pages: 1041-1046
Serial Number: 1001-3695(2024)04-012-1041-06

Publish History

[2023-11-02] Accepted Paper
[2024-04-05] Printed Article

Cite This Article

钟林岚, 张安勤, 田秀霞. 基于周期图卷积与多头注意力GRU组合的交通流量预测模型 [J]. 计算机应用研究, 2024, 41 (4): 1041-1046. (Zhong Linlan, Zhang Anqin, Tian Xiuxia. Traffic flow prediction model based on combining periodic graph convolution network and multi-head attention GRU [J]. Application Research of Computers, 2024, 41 (4): 1041-1046. )

About the Journal

  • 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.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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