Abstract
Predicting traffic flow on traffic networks is a very
challenging task, due to the complicated and dynamic spatial-temporal dependencies between different nodes on the network. The traffic flow renders two types of temporal dependencies, including
short-term neighboring and long-term periodic dependencies. What’s more, the spatial correlations
over different nodes are both local and non-local.
To capture the global dynamic spatial-temporal correlations, we propose a Global Spatial-Temporal
Network (GSTNet), which consists of several layers of spatial-temporal blocks. Each block contains
a multi-resolution temporal module and a global
correlated spatial module in sequence, which can
simultaneously extract the dynamic temporal dependencies and the global spatial correlations. Extensive experiments on the real world datasets verify the effectiveness and superiority of the proposed
method on both the public transportation network
and the road network