App. Res. of Comp. Accepted Paper
Vol. 44, 2027 No. 2

AST‑DGCN: adaptive spatio‑temporal dynamic graph convolutional network for regional traffic congestion prediction

Ju Xiaofei1
Wang Quan2,3
Wu Hao2,3
Kong Lei2,3
1. School of Computer Science and School of Software Engineering, Nanjing University of Information Science and Technology, Nanjing Jiangsu 210044, China
2. School of Internet of Things Engineer, Wuxi University, Wuxi Jiangsu 214105, China
3. Jiangsu Provincial University Key Laboratory of Vehicle-Road Multimodal Perception and Control, Wuxi University, Wuxi Jiangsu 214105, China
DOI: 10.19734/j.issn.1001-3695.2026.06.0176

Abstract

To address the issues of frequent urban traffic congestion, weak anti-noise capability, and inflexible spatio-temporal correlation capture in existing spatio-temporal graph neural networks for refined congestion prediction, this paper proposed a dynamic graph convolutional traffic congestion prediction model that integrates adaptive spatio-temporal interactions, named AST‑DGCN. The congestion evaluation metrics were constructed based on regional inflow and outflow traffic volumes, formulating the task as regional‑level congestion classification. A noise‑adaptive filtering embedding module was designed to preprocess the input data for enhanced robustness, a deformable spatial correlation learning mechanism was adopted to dynamically capture spatial dependencies in the road network, and multi‑scale spatio‑temporal features were fused to mine deep traffic correlations. Experimental results on multiple real‑world datasets show that the proposed model achieves an average improvement of 2.8% in Recall over the state‑of‑the‑art baseline, demonstrating that the method effectively improves both accuracy and robustness of regional traffic congestion prediction.

Foundation Support

江苏省基础研究计划(BK20240315)
国家自然科学基金资助项目(62406229)
江苏省高等学校基础科学(自然科学)研究面上项目合同(24KJB520039)
2023年度江苏省高等学校优秀科技创新团队项目(工业物联网实时方向)
江苏省高等学校基础科学(自然科学)研究面上项目合同(25KJB520046)
无锡市"太湖之光"科技攻关(基础研究)项目(K20253044、K20253037)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2026.06.0176
Publish at: Application Research of Computers Accepted Paper, Vol. 44, 2027 No. 2

Publish History

[2026-10-23] Accepted Paper

Cite This Article

居小斐, 王泉, 吴豪, 等. 融合自适应时空交互的动态图卷积区域交通拥堵预测 [J]. 计算机应用研究, 2027, 44 (2). (2026-10-24). https://doi.org/10.19734/j.issn.1001-3695.2026.06.0176. (Ju Xiaofei, Wang Quan, Wu Hao, et al. AST‑DGCN: adaptive spatio‑temporal dynamic graph convolutional network for regional traffic congestion prediction [J]. Application Research of Computers, 2027, 44 (2). (2026-10-24). https://doi.org/10.19734/j.issn.1001-3695.2026.06.0176. )

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  • Application Research of Computers Monthly Journal
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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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