  Forecasting future traffic flows from previous ones is a challenging problem
because of their complex and dynamic nature of spatio-temporal structures. Most
existing graph-based CNNs attempt to capture the static relations while largely
neglecting the dynamics underlying sequential data. In this paper, we present
dynamic spatio-temporal graph-based CNNs (DST-GCNNs) by learning expressive
features to represent spatio-temporal structures and predict future traffic
flows from surveillance video data. In particular, DST-GCNN is a two stream
network. In the flow prediction stream, we present a novel graph-based
spatio-temporal convolutional layer to extract features from a graph
representation of traffic flows. Then several such layers are stacked together
to predict future flows over time. Meanwhile, the relations between traffic
flows in the graph are often time variant as the traffic condition changes over
time. To capture the graph dynamics, we use the graph prediction stream to
predict the dynamic graph structures, and the predicted structures are fed into
the flow prediction stream. Experiments on real datasets demonstrate that the
proposed model achieves competitive performances compared with the other
state-of-the-art methods.
