  Traffic forecasting is a highly challenging task owing to the dynamical
spatio-temporal dependencies of traffic flows. To handle this, we focus on
modeling the spatio-temporal dynamics and propose a network termed Edge Squeeze
Graph Convolutional Network (ESGCN) to forecast traffic flow in multiple
regions. ESGCN consists of two modules: W-module and ES module. W-module is a
fully node-wise convolutional network. It encodes the time-series of each
traffic region separately and decomposes the time-series at various scales to
capture fine and coarse features. The ES module models the spatio-temporal
dynamics using Graph Convolutional Network (GCN) and generates an Adaptive
Adjacency Matrix (AAM) with temporal features. To improve the accuracy of AAM,
we introduce three key concepts. 1) Using edge features to directly capture the
spatiotemporal flow representation among regions. 2) Applying an edge attention
mechanism to GCN to extract the AAM from the edge features. Here, the attention
mechanism can effectively determine important spatio-temporal adjacency
relations. 3) Proposing a novel node contrastive loss to suppress obstructed
connections and emphasize related connections. Experimental results show that
ESGCN achieves state-of-the-art performance by a large margin on four
real-world datasets (PEMS03, 04, 07, and 08) with a low computational cost.

Machine Learning;Artificial Intelligence