Parallel Self-Learned and Predefined Joint Spatial–Temporal Graph Convolutional Networks for Traffic Flow Prediction

Accurate prediction of spatial–temporal traffic flow drives innovation across various pertinent application domains, including traffic management and route planning. Graph Convolutional Neural Network (GCN) consistently assume a central role within forecasting frameworks. The effectiveness of GCN models significantly hinges on a well-constructed graph structure, whether explicitly defined or acquired through the training process. This structure establishes the mechanism through which messages are exchanged among diverse spatial locations. In the context of traffic flow data, both prior knowledge and unknown factors contribute to the graph structure. Considering both the information derived through algorithms (self-learned) and existing knowledge (predefined), which collectively shape the spatial–temporal patterns of traffic flow, we introduce a novel model named parallel self-learned and predefined joint spatial–temporal GCN (PSPJSTGCN) for traffic flow forecasting. Our model employs a gated mechanism to amalgamate predefined and self-learned graphs in parallel, enabling efficient extraction of spatial–temporal dependency information from both sources. Additionally, we leverage multiscale gated convolution to capture dynamic temporal dependencies across a wide range of receptive fields. We meticulously evaluate our proposed approach using four real-world datasets and substantiate its substantial superiority over prevailing state-of-the-art methods.