In accordance with regulations and requirements, the editorial department's website domain has been changed to arocmag.cn. The original domain (arocmag.com) will be discontinued after Dec. 31st, 2024.
See More
Algorithm Research & Explore
|
App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 1
83-87,93

Traffic flow prediction model based on multi-channel spatial-temporal encoder

Zhang Anqin
Qin Tian
College of Computer Science & Technology, Shanghai University of Electric Power, Shanghai 201306, China
DOI: 10.19734/j.issn.1001-3695.2023.06.0209

Abstract

Traditional traffic flow prediction models model historical data in terms of time and space, ignoring the internal potential temporal periodicity of traffic data and the distance characteristics and similarity spatial characteristics of nodes between traffic networks. Based on this, this paper proposed a multi-channel spatio-temporal encoder model MC-STGNN for traffic flow prediction to improve the accuracy of traffic flow prediction. Firstly, it processed the traffic data into a three channel periodic time series, and encoded the overall sequence data with temporal and adaptive spatial positions to extract dynamic correlations between road network nodes. Secondly, it introduced a multi-heads self-attention mechanism with convolutional structure to capture varying degrees of temporal correlation of periodic data to a greater extent. Finally, it proposed a graph generator to generate a new spatiotemporal map, extracting similarity and distance features between road network nodes, and integrating the spatial information of the original map and the new spatiotemporal map using a gated graph convolutional network. It conducted comprehensive traffic flow prediction experiments for an hour on the highway datasets PEMS03 and PEMS08. The experimental results show that the MC-STGNN model has better performance indicators compared to other baseline models, indicating that the MC-STGNN model has better modeling ability.

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资助项目(07422002)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.06.0209
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 1
Section: Algorithm Research & Explore
Pages: 83-87,93
Serial Number: 1001-3695(2024)01-013-0083-05

Publish History

[2023-09-13] Accepted Paper
[2024-01-05] Printed Article

Cite This Article

张安勤, 秦添. 基于多通道时空编码器的交通流量预测模型 [J]. 计算机应用研究, 2024, 41 (1): 83-87,93. (Zhang Anqin, Qin Tian. Traffic flow prediction model based on multi-channel spatial-temporal encoder [J]. Application Research of Computers, 2024, 41 (1): 83-87,93. )

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.

Indexed & Evaluation

  • The Second National Periodical Award 100 Key Journals
  • Double Effect Journal of China Journal Formation
  • the Core Journal of China (Peking University 2023 Edition)
  • the Core Journal for Science
  • Chinese Science Citation Database (CSCD) Source Journals
  • RCCSE Chinese Core Academic Journals
  • Journal of China Computer Federation
  • 2020-2022 The World Journal Clout Index (WJCI) Report of Scientific and Technological Periodicals
  • Full-text Source Journal of China Science and Technology Periodicals Database
  • Source Journal of China Academic Journals Comprehensive Evaluation Database
  • Source Journals of China Academic Journals (CD-ROM Version), China Journal Network
  • 2017-2019 China Outstanding Academic Journals with International Influence (Natural Science and Engineering Technology)
  • Source Journal of Top Academic Papers (F5000) Program of China's Excellent Science and Technology Journals
  • Source Journal of China Engineering Technology Electronic Information Network and Electronic Technology Literature Database
  • Source Journal of British Science Digest (INSPEC)
  • Japan Science and Technology Agency (JST) Source Journal
  • Russian Journal of Abstracts (AJ, VINITI) Source Journals
  • Full-text Journal of EBSCO, USA
  • Cambridge Scientific Abstracts (Natural Sciences) (CSA(NS)) core journals
  • Poland Copernicus Index (IC)
  • Ulrichsweb (USA)