Learning dynamic and hierarchical traffic spatiotemporal features with Transformer

Understand

Traffic forecasting is an indispensable part of Intelligent transportation systems (ITS), and long-term network-wide accurate traffic speed forecasting is one of the most challenging tasks.

• Recently, deep learning methods have become popular in this domain.
• As traffic data are physically associated with road networks, most proposed models treat it as a spatiotemporal graph modeling problem and use Graph Convolution Network (GCN) based methods.
• These GCN-based models highly depend on a predefined and fixed adjacent matrix to reflect the spatial dependency.