App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 9

Long-term traffic flow prediction method adapted to different traffic flow prediction scenarios

Wang Shuhaia,b
Li Ninga
Pan Xiaoa
Wang Huia
a. School of Information Science and Technology, b. Shijiazhuang Key Laboratory of Artificial Intelligence, Shijiazhuang Tiedao University, Shijiazhuang 050043, China
DOI: 10.19734/j.issn.1001-3695.2026.01.0016

Abstract

To solve the core problems of insufficient spatio-temporal correlation mining, weak heterogeneity fusion, and limited cross-scenario adaptability in long-term traffic flow prediction, we propose a long-term traffic flow prediction method adaptable to different scenarios, aiming to improve prediction accuracy and cross-scenario generalization ability. First, we construct a prompt network that integrates data statistical features, temporal context, and spatial context, and combine it with a generative adversarial network to optimize data distribution, thus enhancing the model’s adaptability to unseen scenarios. Second, we design a spatio-temporal heterogeneity mining module, which captures dynamic temporal heterogeneity and structural spatial heterogeneity accurately through long sequence block embedding, spatio-temporal decoupling masking, and gated fusion mechanisms. Finally, we present a spatio-temporal correlation mining module that uses a multi-masking strategy and bidirectional cross-attention mechanism to deeply mine long-range spatio-temporal correlations and achieve efficient fusion of spatio-temporal heterogeneity and correlation features. We conduct extensive experiments on public datasets. The results show that our method achieves an average improvement of 2%-4% over the suboptimal approaches in terms of MAE, RMSE, and MAPE. By effectively mining spatio-temporal heterogeneity and long-range correlations and improving scenario adaptability, our method overcomes key limitations of existing approaches and significantly enhances the accuracy and generalization of long-term traffic flow prediction.

Foundation Support

河北省自然科学基金资助项目(F2024210042)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2026.01.0016
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 9

Publish History

[2026-05-22] Accepted Paper

Cite This Article

王书海, 李宁, 潘晓, 等. 适应不同场景的交通流量长时预测方法 [J]. 计算机应用研究, 2026, 43 (9). (2026-06-02). https://doi.org/10.19734/j.issn.1001-3695.2026.01.0016. (Wang Shuhai, Li Ning, Pan Xiao, et al. Long-term traffic flow prediction method adapted to different traffic flow prediction scenarios [J]. Application Research of Computers, 2026, 43 (9). (2026-06-02). https://doi.org/10.19734/j.issn.1001-3695.2026.01.0016. )

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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