  Traffic forecasting is one canonical example of spatial-temporal learning
task in Intelligent Traffic System. Existing approaches capture spatial
dependency with a pre-determined matrix in graph convolution neural operators.
However, the explicit graph structure losses some hidden representations of
relationships among nodes. Furthermore, traditional graph convolution neural
operators cannot aggregate long-range nodes on the graph. To overcome these
limits, we propose a novel network, Spatial-Temporal Adaptive graph convolution
with Attention Network (STAAN) for traffic forecasting. Firstly, we adopt an
adaptive dependency matrix instead of using a pre-defined matrix during GCN
processing to infer the inter-dependencies among nodes. Secondly, we integrate
PW-attention based on graph attention network which is designed for global
dependency, and GCN as spatial block. What's more, a stacked dilated 1D
convolution, with efficiency in long-term prediction, is adopted in our
temporal block for capturing the different time series. We evaluate our STAAN
on two real-world datasets, and experiments validate that our model outperforms
state-of-the-art baselines.
