  Accurate forecasting of citywide traffic flow has been playing critical role
in a variety of spatial-temporal mining applications, such as intelligent
traffic control and public risk assessment. While previous work has made
significant efforts to learn traffic temporal dynamics and spatial
dependencies, two key limitations exist in current models. First, only the
neighboring spatial correlations among adjacent regions are considered in most
existing methods, and the global inter-region dependency is ignored.
Additionally, these methods fail to encode the complex traffic transition
regularities exhibited with time-dependent and multi-resolution in nature. To
tackle these challenges, we develop a new traffic prediction
framework-Spatial-Temporal Graph Diffusion Network (ST-GDN). In particular,
ST-GDN is a hierarchically structured graph neural architecture which learns
not only the local region-wise geographical dependencies, but also the spatial
semantics from a global perspective. Furthermore, a multi-scale attention
network is developed to empower ST-GDN with the capability of capturing
multi-level temporal dynamics. Experiments on several real-life traffic
datasets demonstrate that ST-GDN outperforms different types of
state-of-the-art baselines. Source codes of implementations are available at
https://github.com/jill001/ST-GDN.
