  Traffic forecasting is a critical task to extract values from cyber-physical
infrastructures, which is the backbone of smart transportation. However owing
to external contexts, the dynamics at each sensor are unique. For example, the
afternoon peaks at sensors near schools are more likely to occur earlier than
those near residential areas. In this paper, we first analyze real-world
traffic data to show that each sensor has a unique dynamic. Further analysis
also shows that each pair of sensors also has a unique dynamic. Then, we
explore how node embedding learns the unique dynamics at every sensor location.
Next, we propose a novel module called Spatial Graph Transformers (SGT) where
we use node embedding to leverage the self-attention mechanism to ensure that
the information flow between two sensors is adaptive with respect to the unique
dynamic of each pair. Finally, we present Graph Self-attention WaveNet (G-SWaN)
to address the complex, non-linear spatiotemporal traffic dynamics. Through
empirical experiments on four real-world, open datasets, we show that the
proposed method achieves superior performance on both traffic speed and flow
forecasting. Code is available at: https://github.com/aprbw/G-SWaN
