Volume 26 Issue 1
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JIA Xing-li, QU Yuan-hai, ZHU Hao-ran, YANG Hong-zhi, YAO Hui, LI Meng-hui. Research review on STGNN in traffic prediction: From model deconstruction to development path[J]. Journal of Traffic and Transportation Engineering, 2026, 26(1): 46-74. doi: 10.19818/j.cnki.1671-1637.2026.01.003
Citation: JIA Xing-li, QU Yuan-hai, ZHU Hao-ran, YANG Hong-zhi, YAO Hui, LI Meng-hui. Research review on STGNN in traffic prediction: From model deconstruction to development path[J]. Journal of Traffic and Transportation Engineering, 2026, 26(1): 46-74. doi: 10.19818/j.cnki.1671-1637.2026.01.003
shu

Research review on STGNN in traffic prediction: From model deconstruction to development path

doi: 10.19818/j.cnki.1671-1637.2026.01.003
  • 1.

    School of Highway, Chang'an University, Xi'an 710064, Shaanxi, China

  • 2.

    Shaanxi Province Transport Planning Design and Research Institute Co., Ltd., Xi'an 710065, Shaanxi, China

  • 3.

    Road & Bridge International Co., Ltd., Beijing 101113, China

Funds:

National Key R&D Program of China 2020YFC1512003

Science and Technology Project of Shaanxi Department of Transportation 24-41X

Fundamental Research Funds for the Central Universities 300102212203

Technology Innovation Guidance Program of Shaanxi Province (Fund) 2025QCY-KXJ-139

Key R&D Program of Shaanxi Province 2025SF-YBXM-283

More Information
  • Corresponding author: JIA Xing-li, professor, PhD, E-mail: jiaxingli@chd.edu.cn
  • Received Date: 2025-02-10
  • Accepted Date: 2025-06-06
  • Rev Recd Date: 2025-04-19
  • Publish Date: 2026-01-28
    Abstract
  • To clarify the development path of traffic prediction models and to explore future development directions of traffic prediction, a systematic literature analysis approach was adopted, and a technology development direction dominated by spatiotemporal graph neural networks (STGNN) was established. Based on the framework characteristics of STGNNs, a full-process analysis system was constructed, including data preprocessing, static and dynamic graph construction, spatiotemporal feature extraction, and feature fusion. Typical traffic prediction tasks and their corresponding open-source datasets were systematically reviewed. Static graph construction methods based on topological relationships, distance properties, and similarity calculations were summarized, and frontier graph construction technologies were summarized, including direct optimization of dynamic graphs and feature optimization. Current temporal feature modeling methods and spatial feature modeling methods were analyzed from the two dimensions of time and space, and the spatiotemporal feature fusion mechanism was illustrated through two typical cases of Graph WaveNet and DCRNN. For the problems of gradient anomalies and performance degradation in deep network training, general solutions based on information propagation were summarized. The integration paths of emerging technologies were explored, including contrastive learning, pre-training mechanisms, causal reasoning, and mixture-of-experts models with traffic prediction. Analysis results show that applications of STGNNs in traffic prediction have gradually intensified both domestically and internationally, and China ranks first with 1 671 publications in the statistics. Existing studies are mainly focused on improving model memory ability for spatio-temporal features and constructing optimal graph structures, and such optimization schemes have reached a balance between model performance and efficiency. In temporal modeling, a balance between computational efficiency and operational performance is still being explored, while spatial modeling has become the main obstacle to efficiency improvement of existing models. Based on the summary and review of previous studies, future breakthrough directions are expected to be concentrated on the exploration of novel prediction scenarios, improvement of model interpretability, incorporation of real-world physical constraints, innovation of learning strategies, and exploration of industrial-level deployment solutions, so that stronger technical support is provided for intelligent transportation systems.

     

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