  Traffic forecasting is important in intelligent transportation systems of
webs and beneficial to traffic safety, yet is very challenging because of the
complex and dynamic spatio-temporal dependencies in real-world traffic systems.
Prior methods use the pre-defined or learnable static graph to extract spatial
correlations. However, the static graph-based methods fail to mine the
evolution of the traffic network. Researchers subsequently generate the dynamic
graph for each time slice to reflect the changes of spatial correlations, but
they follow the paradigm of independently modeling spatio-temporal
dependencies, ignoring the cross-time spatial influence. In this paper, we
propose a novel cross-time dynamic graph-based deep learning model, named
CDGNet, for traffic forecasting. The model is able to effectively capture the
cross-time spatial dependence between each time slice and its historical time
slices by utilizing the cross-time dynamic graph. Meanwhile, we design a gating
mechanism to sparse the cross-time dynamic graph, which conforms to the sparse
spatial correlations in the real world. Besides, we propose a novel
encoder-decoder architecture to incorporate the cross-time dynamic graph-based
GCN for multi-step traffic forecasting. Experimental results on three
real-world public traffic datasets demonstrate that CDGNet outperforms the
state-of-the-art baselines. We additionally provide a qualitative study to
analyze the effectiveness of our architecture.
