  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. We first build the node-wise graph
according to the road network distance and the edge-wise graph according to
various edge interaction patterns. Then, we implement the interactions of both
nodes and edges using bicomponent graph convolution. The multi-range attention
mechanism is introduced to aggregate information in different neighborhood
ranges and automatically learn the importance of different ranges. Extensive
experiments on two real-world road network traffic datasets, METR-LA and
PEMS-BAY, show that our MRA-BGCN achieves the state-of-the-art results.
