一种面向交通流量预测的自适应时空图卷积网络OA

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,in this study an Adaptive Spa-tio-Temporal Graph Convolutional Network(AdpSTGCN)integrating adaptive graph structure learning with spatio-temporal convolutional architecture was proposes.Firstly,an adaptive graph structure learning method based on node attributes was 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 spa-tio-temporal relationships.A progressive training strategy was introduced to address challenges of excessive learna-ble parameters and data sparsity during model training.Finally,experimental evaluations on highway traffic datasets(METR-La and PEMS-Bay)demonstrated the model's performance in 15,30,and 60 minutes traffic flow predic-tion 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.