Heterogeneity-aware graph convolutional recurrent network for traffic forecasting

Traffic forecasting plays a crucial role in smart city research, significantly influencing urban traffic management and facilitating precise route planning. In recent years, numerous deep learning techniques have gained prominence for their effectiveness in spatiotemporal modeling. However, achieving accurate predictions remains a formidable challenge due to the inherent dynamics and heterogeneity present in spatiotemporal data. To address this challenge, we introduce a novel Heterogeneity-aware Graph Convolutional Recurrent Network (HGCRN) specifically designed for traffic prediction. The HGCRN framework begins by constructing a set of spatial-temporal meta-embeddings that effectively encode temporal context and node heterogeneity. It then utilizes time-varying traffic features to generate dynamic graph learners, integrating dynamic graph convolutional networks with gated recurrent units to proficiently model spatial and temporal dependencies. In the decoding phase, we incorporate temporal embeddings of the target predictions to enhance the forecasting process, thereby improving the model’s capacity to capture trend variations. Extensive experiments conducted on four widely recognized benchmark datasets demonstrate the superiority of the proposed model while highlighting its robust interpretability.