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App. Res. of Comp. Accepted Paper
Vol. 42, 2025 No. 3

Traffic prediction model considering spatial correlation of non-neighboring nodes

Yan Guanghui
Li Hongtao
Zhang Bin
Chang Wenwen
School of Electronic & Information Engineering, Lanzhou Jiaotong University, Lanzhou Gansu 730070, China
DOI: 10.19734/j.issn.1001-3695.2024.07.0261

Abstract

A novel double attention hypergraph convolution neural network (A2HGCN) was proposed to address the challenge of explicitly modeling spatiotemporal correlations between non-adjacent nodes in existing traffic flow prediction models. The method used the construction of hyperedges based on similarities between nodes and creating hypergraphs through node connectivity to represent spatial correlations. A hypergraph convolution model was proposed, which employed hypergraph convolution and line graph convolution after expanding hypergraph lines into graphs to capture potential spatial correlations. Meanwhile, a convolutional long short-term memory network integrated with a double attention mechanism was used to capture temporal features. The algorithm made predictions based on spatial and temporal features. In the PEMS-BAY dataset, the A2HGCN method achieved evaluation metrics of MAE, MAPE, and RMSE at a prediction step of 15 minutes as 1.223, 2.617%, and 2.547, respectively; at 30 minutes as 1.554, 3.541%, and 3.420; and at 60 minutes as 1.867, 4.578%, and 4.224. In the PEMSM dataset, the method achieved evaluation metrics of MAE, MAPE, and RMSE at a prediction step of 15 minutes as 1.858, 4.385%, and 3.339; at 30 minutes as 2.374, 5.775%, and 4.362; and at 60 minutes as 3.046, 7.713%, and 5.479. The results demonstrated that the proposed method outperformed baseline models at different prediction steps, validating the effectiveness of considering spatiotemporal correlations between non-adjacent nodes in enhancing traffic prediction accuracy. It is concluded that hypergraph convolutional neural networks have an advantage in capturing spatiotemporal correlations.

Foundation Support

国家自然科学基金资助项目(62062049)
中央引导地方科技发展资金项目(22ZY1QA005)
甘肃省教育厅青年博士项目(2023QB-038)
2024年研究生教育教学质量提升工程建设项目(JG202418)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.07.0261
Publish at: Application Research of Computers Accepted Paper, Vol. 42, 2025 No. 3

Publish History

[2024-12-10] Accepted Paper

Cite This Article

闫光辉, 李鸿涛, 张斌, 等. 考虑非邻近节点空间相关性的交通流预测模型 [J]. 计算机应用研究, 2025, 42 (3). (2024-12-16). https://doi.org/10.19734/j.issn.1001-3695.2024.07.0261. (Yan Guanghui, Li Hongtao, Zhang Bin, et al. Traffic prediction model considering spatial correlation of non-neighboring nodes [J]. Application Research of Computers, 2025, 42 (3). (2024-12-16). https://doi.org/10.19734/j.issn.1001-3695.2024.07.0261. )

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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