  We all depend on mobility, and vehicular transportation affects the daily
lives of most of us. Thus, the ability to forecast the state of traffic in a
road network is an important functionality and a challenging task. Traffic data
is often obtained from sensors deployed in a road network. Recent proposals on
spatial-temporal graph neural networks have achieved great progress at modeling
complex spatial-temporal correlations in traffic data, by modeling traffic data
as a diffusion process. However, intuitively, traffic data encompasses two
different kinds of hidden time series signals, namely the diffusion signals and
inherent signals. Unfortunately, nearly all previous works coarsely consider
traffic signals entirely as the outcome of the diffusion, while neglecting the
inherent signals, which impacts model performance negatively. To improve
modeling performance, we propose a novel Decoupled Spatial-Temporal Framework
(DSTF) that separates the diffusion and inherent traffic information in a
data-driven manner, which encompasses a unique estimation gate and a residual
decomposition mechanism. The separated signals can be handled subsequently by
the diffusion and inherent modules separately. Further, we propose an
instantiation of DSTF, Decoupled Dynamic Spatial-Temporal Graph Neural Network
(D2STGNN), that captures spatial-temporal correlations and also features a
dynamic graph learning module that targets the learning of the dynamic
characteristics of traffic networks. Extensive experiments with four real-world
traffic datasets demonstrate that the framework is capable of advancing the
state-of-the-art.
