DCENet: A dynamic correlation evolve network for short-term traffic prediction

Abstract

Graph neural networks (GNNs) have been extensively employed in traffic prediction tasks due to their excellent capturing capabilities of spatial dependence. However, the majority of GNN-based approaches tend to employ static graphs, whereas they evolve over time and vary dynamics in real-world traffic situations. It is challenging to capture the dynamic spatial–temporal evolution characteristics of traffic data. To address this problem, we propose a dynamic correlation evolve network (DCENet) for short-term traffic prediction. To be specific, we develop a dynamic correlation self-attention (DCSA) module, which captures dynamic node associations adaptively. In this way, the model acquires new node embedding features without explicitly constructing a new graph structure. Then, an evolution encoder–decoder (EED) module is built to learn the interactions of dynamic features and output future traffic states. The experiments are conducted on two real-world datasets, and the results show that the DCENet outperformers baseline models for most of the cases.