App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 12

Traffic flow prediction based on multi-scale dynamic spatio-temporal attention mechanism

Ding Miaomiao
Ji Yongchang
Jiang Yong
Wang Yixun
Qu Zhijian
School of Computer Science & Technology, Shandong University of Technology, Zibo Shandong 255000, China
DOI: 10.19734/j.issn.1001-3695.2026.05.0120

Abstract

Traffic flow prediction is a core task of intelligent transportation systems. Existing methods suffer from limitations in multi-scale temporal feature extraction and dynamic spatial dependency modeling. This study proposed a traffic flow prediction model named MDSTAN (Multi-Scale Dynamic Spatio-Temporal Attention Network for Traffic Flow Prediction) . In the temporal dimension, a key-value-enhanced convolutional attention module injected local trend information into key-value projections to enhance short-term fluctuation perception, while multi-head self-attention captured long-term periodic dependencies and enabled collaborative representation of multi-scale temporal features. In the spatial dimension, a dual-view dynamic graph convolution mechanism integrated historical pattern priors with real-time state information. A multi-scale semantic unit compression strategy and a two-stage attention interaction structure reduced computational complexity while effectively capturing global semantic dependencies. Experimental results on the PEMS08 dataset showed that the proposed model reduced MAE, RMSE, and MAPE by 6.0%, 2.6%, and 7.0%, respectively, compared with the second-best model. These results demonstrate that the proposed model effectively captures complex spatiotemporal dependencies.

Foundation Support

山东省科技型中小企业创新能力提升工程项目(2025TSGCCZZB0321)
山东省高等学校优秀青年创新团队支持计划项目(2019KIN048)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2026.05.0120
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 12

Publish History

[2026-08-21] Accepted Paper

Cite This Article

丁苗苗, 季永畅, 姜勇, 等. 基于多尺度动态时空注意力机制的交通流预测 [J]. 计算机应用研究, 2026, 43 (12). (2026-08-25). https://doi.org/10.19734/j.issn.1001-3695.2026.05.0120. (Ding Miaomiao, Ji Yongchang, Jiang Yong, et al. Traffic flow prediction based on multi-scale dynamic spatio-temporal attention mechanism [J]. Application Research of Computers, 2026, 43 (12). (2026-08-25). https://doi.org/10.19734/j.issn.1001-3695.2026.05.0120. )

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.

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