Adaptive Graph Spatial Temporal Fourier-Enhanced Transformer Networks for Traffic Prediction

Traffic prediction is an important component of intelligent transportation systems as it plays a key role in route planning and traffic management. However, traffic flow series present a complex spatial-temporal correlations and nonlinear traffic patterns, predicting traffic accurately is made challenging by this. The current methods are struggling to model the overall trend of traffic flow series and are unable to utilize dynamic information about spatial dependencies. In this paper, we propose an adaptive graph spatial temporal Fourier-enhanced transformer networks (ASTFETN) to tackle the above traffic prediction problems. ASTFETN adopts an encoder-decoder architecture, the encoder and decoder are both composed of multiple spatial-temporal blocks to capture dynamic spatial and nonlinear temporal correlations. Furthermore, there is a transformer attention layer to capture the relationships of historical and future time. Experiments on two datasets, METR-LA and PEMS-BAY, demonstrate that ASTFETN outperforms the state-of-the-art baselines.