Graph Transformer-Based Dynamic Edge Interaction Encoding for Traffic Prediction

Traffic prediction is an essential function of intelligent transportation system for traffic control and autonomous driving. Most existing methods encode traffic spatial and temporal data separately, and then design a feature fusion module to correlate spatial and temporal features. However, spatial information is often static, and repetitive static spatial encoding leads to waste of resources, especially in large-scale traffic network prediction. In this paper, we propose a dynamic edge interaction encoding method for spatio-temporal features based on inverse Transformer (iTransformer) and Graph Transformer, named iTPGT-former. The dynamic edge interaction process is designed to embed dynamic temporal features into static edges via a convolutional embedding module. To enhance the Graph Transformer, a relative position encoding strategy based on the self-attentive score of the positive definite kernel (PDK) on graphs and a method for graph substructure encoding (GSE) via enumeration of paths are introduced. In the experimental and discussion session, the iTPGT-former is considered for accuracy, parameters, inference speed, and rich ablation experiments are provided based on six publicly available traffic datasets. The results show that iTPGT-former outperforms the baseline model in both traffic flow and traffic speed prediction. The maximum improvement is achieved in the METR-LA 60-min speed prediction task, with 15.2% reduction in Mean Absolute Percentage Error (MAPE). In addition, the inference of iTPGT-former is significantly faster than the GCN-based method. Our implementation of the iTPGT-former is available at https://github.com/ouyangnann/iTPGTN-former.