Dynamic Graph Convolutional Recurrent Network With Temporal Self-Attention for Accurate Traffic Flow Prediction

Abstract

Accurate traffic flow prediction is essential for the effective management of Intelligent Transportation Systems (ITS). However, traditional methods based on static graph structures often fail to address the complex and nonlinear spatiotemporal dependencies in evolving traffic conditions. To address this challenge, we propose a Dynamic Graph Convolutional Recurrent Network with Temporal Self-Attention (DGCRN-TSA), which integrates a temporal attention mechanism to jointly capture dynamic spatial topologies and long-range temporal patterns. The model incorporates a graph generation module that adaptively learns time-varying adjacency matrices from traffic signals and introduces a trend-aware attention module enhanced by residual-guided decomposition for distinguishing between normal and anomalous traffic behaviours. Experiments on real traffic datasets confirm that DGCRN-TSA achieves superior performance in both short- and medium-to-long-term forecasts. Notably, it reduces MAE by 19.4% on PeMS04 and improves MAPE by 12.2% on PeMS08. The model also ensures high prediction accuracy with strong computational efficiency and an inference speed comparable to AGCRN. DGCRN-TSA offers an efficient and reliable solution for dynamic spatiotemporal modelling and large-scale real-time traffic prediction.