Local-Global Dynamic Information Fusion Graph Learning for Traffic Flow Prediction

In the field of intelligent transportation systems, accurately predicting traffic flow is a challenging endeavor for enabling efficient services in urban road networks. While current research has made significant advancements in capturing nonlinear features and spatiotemporal dependencies at a fixed geographic node level, it often overlooks the dynamic interactions of traffic flow across different regions. In this work, we present a novel local-global dynamic information fusion graph learning model (LGDF-GL) for traffic flow prediction. Different from other graph learning model, we first design a dynamic adjacency matrix by region-adaptive parameter learning, and fuse it with a static adjacency matrix based on node geographic information by dynamic information fusion module, and the fused result serves as the diagonal elements of the local adjacency matrix. Subsequently, we input it into the graph convolutional network (GCN) and gated recurrent unit (GRU) aggregation model to extract local feature information containing dynamic spatiotemporal features from traffic flow data. The local feature information is pruned and concatenated to obtain global feature representations. Then we achieve global feature representation prediction through multiple fully connected layers. Finally, we conducted experiments on two real-world traffic flow datasets to evaluate the performance of our proposed method. The results clearly demonstrate that the LGDF-GL method achieves superior prediction ability compared to several baseline models.