Research Article
A Novel Multi-graph Spatial-Temporal Graph Neural Network Security Model for Traffic Prediction
@INPROCEEDINGS{10.1007/978-3-032-23453-7_13, author={Chunyan Diao and Yanrong Zhang and Jinquan Ma and Xi Ouyang and Lixiang Guo and Nan Zhou and Sheng Huo and Tong Shen and Kuanching Li}, title={A Novel Multi-graph Spatial-Temporal Graph Neural Network Security Model for Traffic Prediction}, proceedings={Security and Privacy in Communication Networks. 21st EAI International Conference, SecureComm 2025, Xiangtan, China, July 4--6, 2025, Proceedings, Part III}, proceedings_a={SECURECOMM}, year={2026}, month={11}, keywords={Gated recurrent network Graph conventional network Multi-Graph Spatial-temporal system vehicle prediction}, doi={10.1007/978-3-032-23453-7_13} }- Chunyan Diao
Yanrong Zhang
Jinquan Ma
Xi Ouyang
Lixiang Guo
Nan Zhou
Sheng Huo
Tong Shen
Kuanching Li
Year: 2026
A Novel Multi-graph Spatial-Temporal Graph Neural Network Security Model for Traffic Prediction
SECURECOMM
Springer
DOI: 10.1007/978-3-032-23453-7_13
Abstract
With the acceleration of urbanization, traffic security has become a top priority in the construction of smart cities. Efficient and accurate traffic flow prediction can bring immense convenience to people’s lives, by connecting different networks and optimizing transport systems as a whole. The unique non-linearity and complex spatial-ST-correlation of transport flow data suggest considerable challenges in prediction, as existing spatial-temporal prediction algorithms are based on graph convolution to capture global or heterogeneous relationships, and simpler graph convolution models cannot accurately capture complex spatial relationships. To address the aforementioned issues, this article presents a multi-graph-based spatial-temporal GCN model, built up of different connected graphs to capture fine-grained spatial relationships. Experimental evaluations show that the framework proposed outperforms existing methods with better results in the analysis performed with publicly available datasets and proved that the proposed security prediction model can provide a decision-making basis for solving traffic security problems.