Spatio-Temporal Joint Graph Convolutional Networks for Traffic Forecasting. (arXiv:2111.13684v1 [cs.LG])

Recent studies focus on formulating the traffic forecasting as a
spatio-temporal graph modeling problem. They typically construct a static
spatial graph at each time step and then connect each node with itself between
adjacent time steps to construct the spatio-temporal graph. In such a graph,
the correlations between different nodes at different time steps are not
explicitly reflected, which may restrict the learning ability of graph neural
networks. Meanwhile, those models ignore the dynamic spatio-temporal
correlations among nodes as they use the same adjacency matrix at different
time steps. To overcome these limitations, we propose a Spatio-Temporal Joint
Graph Convolutional Networks (STJGCN) for traffic forecasting over several time
steps ahead on a road network. Specifically, we construct both pre-defined and
adaptive spatio-temporal joint graphs (STJGs) between any two time steps, which
represent comprehensive and dynamic spatio-temporal correlations. We further
design dilated causal spatio-temporal joint graph convolution layers on STJG to
capture the spatio-temporal dependencies from distinct perspectives with
multiple ranges. A multi-range attention mechanism is proposed to aggregate the
information of different ranges. Experiments on four public traffic datasets
demonstrate that STJGCN is computationally efficient and outperforms 11
state-of-the-art baseline methods.



Related post