Spatial-Temporal Traffic Prediction Model Based on Adaptive Graphs Fusion and Dual-Graph Collaborative Convolution

Traffic flow prediction is crucial for intelligent transportation systems (ITS). Traditional graph convolutional networks (GCNs) have limitations in handling road network data. These GCNs can only handle binary relationships between nodes and cannot effectively capture the dynamic and nonlinear spatial dependencies among multiple nodes in the road network. This paper proposes an innovative spatial-temporal traffic flow prediction model to address this challenge. Firstly, by introducing novel graphs fusion and hypergraph encoding module, a fused graph and its dual hypergraph are constructed to provide richer structural information for the model. Then, the model can learn complex relationships among multiple nodes through the collaborative convolution of GCN and hypergraph convolutional network (HGCN). To comprehensively capture the dynamic nature of traffic flow, we utilize a variant of Transformer combined with time encoding information to capture the periodicity in the data, thereby enhancing the model's ability to recognize periodic traffic flow patterns. Comprehensive experimental results on two publicly accessible real-world traffic datasets demonstrate the superiority of our proposed model over state-of-the-art traffic prediction models.