  Accurate traffic forecasting is vital to an intelligent transportation
system. Although many deep learning models have achieved state-of-art
performance for short-term traffic forecasting of up to 1 hour, long-term
traffic forecasting that spans multiple hours remains a major challenge.
Moreover, most of the existing deep learning traffic forecasting models are
black box, presenting additional challenges related to explainability and
interpretability. We develop Graph Pyramid Autoformer (X-GPA), an explainable
attention-based spatial-temporal graph neural network that uses a novel pyramid
autocorrelation attention mechanism. It enables learning from long temporal
sequences on graphs and improves long-term traffic forecasting accuracy. Our
model can achieve up to 35 % better long-term traffic forecast accuracy than
that of several state-of-the-art methods. The attention-based scores from the
X-GPA model provide spatial and temporal explanations based on the traffic
dynamics, which change for normal vs. peak-hour traffic and weekday vs. weekend
traffic.
