Enhancing Traffic Flow Forecasting With Delay Propagation: Adaptive Graph Convolution Networks for Spatio-Temporal Data

Traffic flow Forecasting is essential in intelligent transportation systems. Although graph neural networks perform well with non-Euclidean traffic data, they exhibit limits in accurately capturing complex spatio-temporal dependencies. Most current methodologies assume instantaneous propagation of traffic flow information, neglecting the delay in information propagation between nodes. Moreover, traditional graph convolutional models capture spatial dependencies using static adjacency matrices, overlooking the dynamic correlations among nodes over time. To address these challenges, this paper proposes a delay propagation spatio-temporal graph convolutional network (DPSTGC) for traffic prediction. By incorporating the delay propagation of traffic flow information into a directed graph of the road network, this model accurately captures the spatio-temporal dependencies of traffic flows. To refine the relationships between nodes, adaptive graph convolution network is employed to learn the dynamic correlation between nodes. The alternating application of temporal gated convolutions and spatial structure significantly enhances the model’s capacity to interpret spatio-temporal information. Furthermore, the effectiveness of the Delay-aware Directed Graph Attention (DDGA) is further interpreted from a causality perspective. Finally, the proposed model is evaluated on four real-world traffic datasets for experimental validation. Experimental results demonstrate that DPSTGC proficiently captures spatio-temporal information and achieves excellent performance.