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App. Res. of Comp. Accepted Paper
Vol. 42, 2025 No. 12

Multi-dimensional feature fusion and residual-enhanced learning for traffic flow prediction

Zhang Zhenlin1
Guo Huijie1
Dou Tianfeng1
Qi Kaiyuan2
Wu Dong2
Qu Zhijian1
Ren Chongguang1
1. Shandong University of Technology, School of Computer Science & Technology, Zibo Shandong 255000, China
2. Inspur (Jinan) Data Technology Co, Ltd, Jinan Shandong 250101, China
DOI: 10.19734/j.issn.1001-3695.2025.06.0168

Abstract

Traffic flow forecasting plays a crucial role in intelligent transportation systems. To address the limitations of existing methods in feature utilization and spatiotemporal dependency modeling, this paper developed a novel model named MFRGCRN (Multi-dimensional Feature Fusion and Residual-enhanced Graph Convolutional Recurrent Network) . The model combines autoencoders, depthwise separable convolutions, and temporal convolutions to comprehensively capture spatiotemporal correlations. It integrates gated recurrent units with a multi-scale convolutional attention mechanism to learn complex dependencies, and adopts a multi-scale residual enhancement module to progressively model dynamic traffic patterns. Experimental results on four real-world datasets demonstrate that the proposed model consistently outperforms baseline methods in prediction accuracy. In particular, on the 12-step forecasting task of the PEMS08 dataset, MFRGCRN achieves reductions of approximately 7.7% in MAE, 2.9% in RMSE, and 4.5% in MAPE, highlighting its superior long-term prediction performance. The model exhibits strong accuracy, stability, and robustness, providing an effective solution for complex traffic flow modeling in intelligent transportation systems.

Foundation Support

山东省高等学校优秀青年创新团队项目(2019KJN048)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.06.0168
Publish at: Application Research of Computers Accepted Paper, Vol. 42, 2025 No. 12

Publish History

[2025-08-21] Accepted Paper

Cite This Article

张振琳, 郭慧洁, 窦天凤, 等. 基于多维特征融合与残差增强的交通流量预测 [J]. 计算机应用研究, 2025, 42 (12). (2025-08-21). https://doi.org/10.19734/j.issn.1001-3695.2025.06.0168. (Zhang Zhenlin, Guo Huijie, Dou Tianfeng, et al. Multi-dimensional feature fusion and residual-enhanced learning for traffic flow prediction [J]. Application Research of Computers, 2025, 42 (12). (2025-08-21). https://doi.org/10.19734/j.issn.1001-3695.2025.06.0168. )

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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