Traffic flow prediction is a hot research area in intelligent transportation systems,and the fundamental challenge is to effectively model the complex spatial-temporal correlations in traffic data. To address the problem that most existing spatial-temporal Transformer models ignore the important effects of temporal trend and spatial heterogeneity when constructing spatial-temporal correlation matrices,a traffic flow prediction model based on Spatial-Temporal Aware Transformer (STAFormer) is proposed. First,an improved spatial-temporal aware self-attention mechanism is used to mine potential temporal trend and spatial heterogeneity features in traffic flow data,establishing an accurate spatial-temporal correlation matrix to obtain global spatial-temporal features. Then,the multi-range diffusion convolution is used to simulate the multi-order diffusion process of traffic flow in the road network to capture the local spatial features. Finally,the multivariate feature fusion module is used to adaptively fuse the captured spatial-temporal features and output the prediction results. Experiments are conducted on two real traffic datasets,i.e. PeMS04 and PeMS08,and the results show that,compared with the recently proposed Transformer-based models such as RPConvformer,ASTGNN,and PDFormer,the mean absolute errors of STAFormer are reduced by 8.0％,6.5％,and 2.0％,respectively.
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