Accurate traffic prediction is a challenging task in intelligent
transportation systems because of the complex spatio-temporal dependencies in
transportation networks. Many existing works utilize sophisticated temporal
modeling approaches to incorporate with graph convolution networks (GCNs) for
capturing short-term and long-term spatio-temporal dependencies. However, these
separated modules with complicated designs could restrict effectiveness and
efficiency of spatio-temporal representation learning. Furthermore, most
previous works adopt the fixed graph construction methods to characterize the
global spatio-temporal relations, which limits the learning capability of the
model for different time periods and even different data scenarios. To overcome
these limitations, we propose an automated dilated spatio-temporal synchronous
graph network, named Auto-DSTSGN for traffic prediction. Specifically, we
design an automated dilated spatio-temporal synchronous graph (Auto-DSTSG)
module to capture the short-term and long-term spatio-temporal correlations by
stacking deeper layers with dilation factors in an increasing order. Further,
we propose a graph structure search approach to automatically construct the
spatio-temporal synchronous graph that can adapt to different data scenarios.
Extensive experiments on four real-world datasets demonstrate that our model
can achieve about 10% improvements compared with the state-of-art methods.
Source codes are available at https://github.com/jinguangyin/Auto-DSTSGN