Multi-Range Attentive Bicomponent Graph Convolutional Network for Traffic Forecasting

Understand

Traffic forecasting is of great importance to transportation management and public safety, and very challenging due to the complicated spatial-temporal dependency and essential uncertainty brought about by the road network and traffic conditions.

• Latest studies mainly focus on modeling the spatial dependency by utilizing graph convolutional networks (GCNs) throughout a fixed weighted graph.
• However, edges, i.e., the correlations between pair-wise nodes, are much more complicated and interact with each other.
• In this paper, we propose the Multi-Range Attentive Bicomponent GCN (MRA-BGCN), a novel deep learning model for traffic forecasting.