STGHTN: Spatial-temporal gated hybrid transformer network for traffic flow forecasting

Accurate traffic forecasting is a critical function of intelligent transportation systems, which remains challenging due to the complex spatial and temporal dependence of traffic data. GNN-based traffic forecasting models typically utilize predefined graphical structures based on prior knowledge and do not adapt well to dynamically changing traffic characteristics, which may limit their performance. The transformer is a compelling architecture with an innate global self-attention mechanism, but cannot capture low-level detail very well. In this paper, we propose a novel Spatial-Temporal Gated Hybrid Transformer Network (STGHTN), which leverages local features from temporal gated convolution, spatial gated graph convolution respectively and global features by transformer to further improve the traffic flow forecasting results. First, in the temporal dimension, we take full advantage of the local properties of temporal gated convolution and the global properties of transformer to effectively fuse short-term and long-term temporal dependence. Second, we mutually integrate two modules to complement each representation by utilizing spatial gated graph convolution to extract local spatial dependence and transformer to extract global spatial dependence. Furthermore, we propose a multi-graph model that constructs a road connection graph, a similarity graph, and an adaptive dynamic graph to exploit the static and dynamic associations between road networks. Experiments on four real datasets confirm the proposed method's state-of-the-art performance. Our implementation of the STGHTN code via PyTorch is available at hups://github.comManSoL/STGHTN.