  Accurate and real-time traffic forecasting plays an important role in the
Intelligent Traffic System and is of great significance for urban traffic
planning, traffic management, and traffic control. However, traffic forecasting
has always been considered an open scientific issue, owing to the constraints
of urban road network topological structure and the law of dynamic change with
time, namely, spatial dependence and temporal dependence. To capture the
spatial and temporal dependence simultaneously, we propose a novel neural
network-based traffic forecasting method, the temporal graph convolutional
network (T-GCN) model, which is in combination with the graph convolutional
network (GCN) and gated recurrent unit (GRU). Specifically, the GCN is used to
learn complex topological structures to capture spatial dependence and the
gated recurrent unit is used to learn dynamic changes of traffic data to
capture temporal dependence. Then, the T-GCN model is employed to traffic
forecasting based on the urban road network. Experiments demonstrate that our
T-GCN model can obtain the spatio-temporal correlation from traffic data and
the predictions outperform state-of-art baselines on real-world traffic
datasets. Our tensorflow implementation of the T-GCN is available at
https://github.com/lehaifeng/T-GCN.
