Algorithm Research & Explore
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440-444

Traffic flow prediction model based on graph convolutional network for traffic intersections

He Qinwei
Li Xuejun
Liao Jing
College of Computer Science &Technology, Southwest University of Science & Technology, Mianyang Sichuan 621000, China

Abstract

Traffic flow prediction is an important and challenging task in building smart city. Accurate forecasting requires consideration of spatio-temporal characteristics consisting of multiple influences such as holiday, similar node, and weather. In order to accurately capture the spatio-temporal characteristics of road network intersections, this paper proposed a prediction model based on graph convolutional neural network, temporal algorithm Prophet and Pearson correlation coefficient to achieve accurate prediction of intersection traffic considering spatial structure, similar nodes, holidays and other influencing factors. Firstly, it introduced pearson correlation coefficient to reduce the influence of similar nodes to improve the temporal algorithm for capturing temporal features. Secondly, it used the graph convolution neural network to capture spatial features; Finally, it determined the fusion weights of the graph convolutional network and the temporal algorithm by linear regression to obtain the results of spatio-temporal fusion prediction. This paper finally extracted the intersection traffic data based on the analysis of Chengdu taxi trajectory data and conducted traffic prediction experiments. The results show that the accuracy of the model proposed in this paper is better than that of most existing baseline methods, compared with the T-GCN, ASTGCN, and AGCRN models, the MAE is reduced by 1.623, 0.724, 0.161, respectively, and the accuracy is improved by 0.144, 0.068, and 0.021, respectively, which verifies the effectiveness of the model in traffic intersection flow prediction.

Foundation Support

国防基础计划科研项目(JCKY2019204B007)
国家自然基金资助项目(61872304)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2022.06.0307
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 2
Section: Algorithm Research & Explore
Pages: 440-444
Serial Number: 1001-3695(2023)02-021-0440-05

Publish History

[2022-08-29] Accepted Paper
[2023-02-05] Printed Article

Cite This Article

何沁玮, 李学俊, 廖竞. 基于图卷积网络的交通路口流量预测模型 [J]. 计算机应用研究, 2023, 40 (2): 440-444. (He Qinwei, Li Xuejun, Liao Jing. Traffic flow prediction model based on graph convolutional network for traffic intersections [J]. Application Research of Computers, 2023, 40 (2): 440-444. )

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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