Global Attention‐Based Dynamic Multi‐Graph Convolutional Recurrent Network for Traffic Flow Forecasting

ABSTRACT Accurate traffic flow forecasting is a challenging task in intelligent transportation system. With traffic flow forecasting being formulated as a spatio‐temporal graph modelling problem, graph convolution network (GCN) is increasingly used in recent research. However, most approaches employ a single predefined or adaptive graph for convolution, which cannot adequately represent complicated dependencies inherent in real‐world traffic flow data. And they are limited in learning relationships between long‐distance time steps. To address these concerns, we propose a global attention‐based dynamic multi‐graph convolutional recurrent network (GA‐DMGCRN). Specifically, we design a dynamic multi‐graph convolution module based on dynamic graph learning network that generates graphs by adjusting to time‐varying input data throughout the training and testing phases, allowing for the effective extraction of dynamic spatial and semantic dependencies. To capture temporal features, we propose the dynamic multi‐graph convolution recurrent unit, and multihead ProbSparse self‐attention with linear biases is developed to model global temporal dependencies. The proposed GA‐DMGCRN is evaluated on three real traffic datasets. Compared with the baseline models, our model achieves an average improvement of 1.97%, 3.11%, and 2.01% under MAE, RMSE, and MAPE metrics, which can provide real‐world value by improving traffic efficiency, mitigating congestion, and optimizing route planning.