Dynamic Graph Convolutional Recurrent Network With Spatiotemporal Category Information Embedding for Traffic Flow Prediction

Traffic flow prediction is a challenging spatiotemporal prediction task due to its spatiotemporal dynamics and uncertainty. In recent years, graph convolutional neural networks (GCNs) have been applied to traffic flow prediction due to their exceptional capabilities in processing network information. However, current GCN-based methods fail to capture long-range spatial dependencies between regions with similar functionalities, and the modeling of spatial dependencies within adjacent time steps is neglected. To solve these problems, this work proposes a new dynamic graph convolutional recurrent network (CDGCRN) with the spatiotemporal category information embedding. An embedding layer with spatiotemporal category information is introduced to enhance the model’s capability in capturing long-range spatial dependencies. This layer aids the model in rapidly identifying traffic patterns with distinct features. Morever, Dynamic spatial information fusion graph convolutional recurrent unit is proposed to capture spatial dependencies within all time steps and adjacent time steps. It incorporates a learnable graph structure with a time window for capturing spatial dependency relationships within adjacent time steps and a globally learnable graph structure for learning spatial dependency relationships across all time steps. Experimental results on the four real-world data sets demonstrate that CDGCRN is not only computationally efficient but also outperforms 17 state-of-the-art baseline methods.