  Accurate traffic forecasting is essential for effective urban planning and
congestion management. Deep learning (DL) approaches have gained colossal
success in traffic forecasting but still face challenges in capturing the
intricacies of traffic dynamics. In this paper, we identify and address this
challenges by emphasizing that spatial features are inherently dynamic and
change over time. A novel in-depth feature representation, called Dynamic
Spatio-Temporal (Dyn-ST) features, is introduced, which encapsulates spatial
characteristics across varying times. Moreover, a Dynamic Spatio-Temporal Graph
Transformer Network (DST-GTN) is proposed by capturing Dyn-ST features and
other dynamic adjacency relations between intersections. The DST-GTN can model
dynamic ST relationships between nodes accurately and refine the representation
of global and local ST characteristics by adopting adaptive weights in low-pass
and all-pass filters, enabling the extraction of Dyn-ST features from traffic
time-series data. Through numerical experiments on public datasets, the DST-GTN
achieves state-of-the-art performance for a range of traffic forecasting tasks
and demonstrates enhanced stability.
