Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 39, 2022 No. 8
2301-2305

Traffic flow prediction based on gated recurrent graph convolutional network

Wang Ming1
Peng Jian1
Huang Feihu1,2
1. College of Computer Science, Sichuan University, Chengdu 610065, China
2. Aostar Information Technologies Co. , Ltd. , Chengdu 610041, China
DOI: 10.19734/j.issn.1001-3695.2022.01.0026

Abstract

Traffic flow prediction plays a key role in the construction of intelligent transportation systems. However, existing prediction methods cannot mine the potential spatiotemporal correlation in the data accurately, and most of them use fully connected networks for single-step prediction. In order to further mine the spatial-temporal features of the data and improve the accuracy of long-term and short-term prediction tasks, this paper proposed a gated recurrent graph convolutional network(GR-GCN). Firstly, by using spectral graph convolution, it combined with gated recurrent unit(GRU) to construct spatial-temporal components(STC) to capture the spatial-temporal correlation of nodes in the network, fully extracted the spatial-temporal features of the data. Secondly, it used STC as an encodered unit and enter the time sequence data together with road network data into it. Finally, using gated recurrent unit as a decoder unit, and combined them into an encoder-decoder network structure(encoder-decoder) in chronological order, and decoded the prediction results at each moment in turn. It carried out the experiments on the highway datasets PeMSD4 and PeMSD8 in the California Department of Transportation(Caltrans) performance evaluation system. The results show that model GR-GCN is better than most existing benchmark models in predicting the traffic flow in the future 15 min, 30 min, 45 min and 60 min, especially in long-term prediction.

Foundation Support

四川省重点研发计划资助项目(2020YFG0089,22ZDYF3599,2020YFG0304,2020YFG0308)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2022.01.0026
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 8
Section: Algorithm Research & Explore
Pages: 2301-2305
Serial Number: 1001-3695(2022)08-010-2301-05

Publish History

[2022-03-25] Accepted Paper
[2022-08-05] Printed Article

Cite This Article

汪鸣, 彭舰, 黄飞虎. 基于门控循环图卷积网络的交通流预测 [J]. 计算机应用研究, 2022, 39 (8): 2301-2305. (Wang Ming, Peng Jian, Huang Feihu. Traffic flow prediction based on gated recurrent graph convolutional network [J]. Application Research of Computers, 2022, 39 (8): 2301-2305. )

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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