Spatial-Temporal Graph Attention Gated Recurrent Transformer Network for Traffic Flow Forecasting

With the significant increase in the number of motor vehicles, road-related issues such as traffic congestion and accidents have also escalated. The development of an accurate and efficient traffic flow forecasting model is essential for helping car owners plan their journeys. Despite advancements in forecasting models, there are three remaining issues: (i) failing to effectively use cyclical data; (ii) failing to adequately capture spatial dependencies; and (iii) high time complexity and memory usage. To tackle the aforementioned challenges, we present a novel Spatial-Temporal Graph Attention Gated Recurrent Transformer Network (STGAGRTN) for traffic flow forecasting. Specifically, the use of a Spatial Transformer module allows for the extraction of dynamic spatial dependencies among individual nodes, going beyond the limitation of only considering neighboring nodes. Subsequently, we propose a Temporal Transformer to extract periodic information from traffic data and capture long-term dependencies. Additionally, we utilize two additional classical techniques to complement the aforementioned modules for extracting characteristics. By incorporating comprehensive spatial-temporal characteristics into our model, we can accurately predict multiple nodes simultaneously. Finally, we have successfully optimized the computational complexity of the Transformer module from O(n2) to O(nlogn). Our model has undergone extensive testing on four authentic datasets, providing compelling evidence of its superior predictive capabilities.