Traffic forecasting is crucial to intelligent transportation system, and very challenging due to the uncertainty and complexity of spatial-temporal dependencies in real-world traffic network. Many existing approaches use the pre-defined graph to model spatial correlations, but they fail to capture the latent spatial evolution. Then some dynamic graph-based methods are proposed to address this issue, however they separately model spatial and temporal dependencies without internal connection. In this paper, we propose a novel Dynamic gated Spatia...
