Spatio-temporal Fourier enhanced heterogeneous graph learning for traffic forecasting

Traffic flow prediction is of paramount importance in the field of spatio-temporal forecasting. In recent years, research efforts have primarily been directed towards developing intricate graph convolutional networks (GCNs) to capture spatial complexities. However, this has inadvertently led to the neglect of the inherent temporal correlations in traffic prediction, as well as the heterogeneity of graph structures. As a result, existing models show limited efficacy when dealing with the complex nature of traffic data. To address this issue, this paper introduces a novel traffic prediction model: the fourier-enhanced heterogeneous graph convolution attention recurrent network (FEHGCARN). This model integrates historical information and incorporates a graph convolution attention recurrent unit (GCARU), meticulously engineered to effectively capture spatio-temporal dependencies. Additionally, it features a fourier-enhanced heterogeneous graph learning module, which facilitates the acquisition of complex relationships among nodes in the frequency domain. Notably, this memory network excels at recognizing abrupt traffic conditions. To validate our approach, we conducted comprehensive comparisons using three authentic datasets and benchmarked our model against six state-of-the-art baseline methods. The experimental results unequivocally demonstrate the superior performance of our model across all evaluation metrics.