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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 39, 2022 No. 8
2306-2310

Traffic flow prediction based on adaptive gated graph neural network

Wang Yang
Zheng Jin
Liu Ying
Li Ping
College of Computer Science, Southwest Petroleum University, Chengdu 610500, China
DOI: 10.19734/j.issn.1001-3695.2022.01.0017

Abstract

Traffic flow prediction is an important part of intelligent transportation system. Due to the complexity of traffic data, long-term and accurate traffic flow prediction has always been one of the most challenging tasks in time series forecasting. In recent years, researchers have applied spatial-temporal graph modeling methods based on graph neural networks to traffic flow prediction tasks which achieved good prediction performance. However, existing graph modeling methods only reflect the spatial dependence in road networks through predefined adjacency structures, ignoring the importance of time-series correlation between nodes for prediction. Aiming at this limitation, this paper proposed an adaptive gated graph neural network(Ada-GGNN), the core of which was to simultaneously capture the spatial structure of the road network and adaptive time series correlation through the spatial passing module, and learned time-series features on nodes through the gating mechanism. The experimental results on two real-world traffic network datasets PeMSD7 and Los-loop show that the model has better performance.

Foundation Support

国家杰出青年科学基金资助项目(61625204)
四川省科技计划资助项目(2020YJ0125)
西南石油大学科研创新能力提升计划“启航”项目(2019QHZ016)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2022.01.0017
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 8
Section: Algorithm Research & Explore
Pages: 2306-2310
Serial Number: 1001-3695(2022)08-011-2306-05

Publish History

[2022-03-22] Accepted Paper
[2022-08-05] Printed Article

Cite This Article

王杨, 郑津, 刘影, 等. 基于自适应门控图神经网络的交通流预测 [J]. 计算机应用研究, 2022, 39 (8): 2306-2310. (Wang Yang, Zheng Jin, Liu Ying, et al. Traffic flow prediction based on adaptive gated graph neural network [J]. Application Research of Computers, 2022, 39 (8): 2306-2310. )

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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