Adaptive Spatial-temporal Graph Convolutional Network for Traffic Forecasting

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Adaptive Spatial-temporal Graph Convolutional Network for Traffic Forecasting

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[1]ZHANG Zhen,LIU Bo,LI Zhuo,et al.Adaptive Spatial-temporal Graph Convolutional Network for Traffic Forecasting[J].Journal of Zhengzhou University (Engineering Science),2027,48(XX):1-9.[doi:10.13705/j.issn.1671-6833.2025.05.011]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 48 Number of periods: 2027 XX Page number: 1-9 Column: Public date: 2027-12-10

Title:: Adaptive Spatial-temporal Graph Convolutional Network for Traffic Forecasting

Author(s):: ZHANG Zhen^1,2, LIU Bo¹, LI Zhuo², ZHANG Xuezhong³; 1. School of Henan Institute of Advanced Technology, Zhengzhou University, Zhengzhou 450001, China; 2. School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China; 3. State Grid Zhoukou Power Supply Company, Zhoukou 466000, China

Keywords:: traffic flow prediction; adaptive graph structure; node attributes; graph convolutional network; spatiotemporal correlation

CLC:: TP391

DOI:: 10.13705/j.issn.1671-6833.2025.05.011

Abstract:: To address the limitations of existing traffic flow prediction methods in fully utilizing node attributes to guide graph structure learning and capturing complex spatio-temporal dependencies, this study proposes an Adaptive Spatio-Temporal Graph Convolutional Network (AdpSTGCN) integrating adaptive graph structure learning with spatio-temporal convolutional architecture. Firstly, an adaptive graph structure learning method based on node attributes is designed to dynamically capture spatial relationships in road networks from both global and local perspectives. Secondly, a dedicated spatio-temporal convolutional architecture was developed to effectively model spatio-temporal correlations in traffic flow patterns, further enhancing the model’s capability to handle complex spatio-temporal relationships. A progressive training strategy is introduced to address challenges of excessive learnable parameters and data sparsity during model training. Finally, experimental evaluations on highway traffic datasets (METR-LA and PEMS-Bay) demonstrate the model’s performance in 15, 30, and 60 minutes traffic flow prediction tasks. Experimental results showed that the AdpSTGCN model achieved the best performance among multiple baseline models in terms of three prediction error metrics: MAE, RMSE, and MAPE. These findings indicate the model’s superior modeling capabilities for both short-term and long-term traffic flow prediction tasks, providing a theoretical foundation for urban traffic management strategies.

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