  Accurate real-time traffic forecasting is a core technological problem
against the implementation of the intelligent transportation system. However,
it remains challenging considering the complex spatial and temporal
dependencies among traffic flows. In the spatial dimension, due to the
connectivity of the road network, the traffic flows between linked roads are
closely related. In terms of the temporal factor, although there exists a
tendency among adjacent time points in general, the importance of distant past
points is not necessarily smaller than that of recent past points since traffic
flows are also affected by external factors. In this study, an attention
temporal graph convolutional network (A3T-GCN) traffic forecasting method was
proposed to simultaneously capture global temporal dynamics and spatial
correlations. The A3T-GCN model learns the short-time trend in time series by
using the gated recurrent units and learns the spatial dependence based on the
topology of the road network through the graph convolutional network. Moreover,
the attention mechanism was introduced to adjust the importance of different
time points and assemble global temporal information to improve prediction
accuracy. Experimental results in real-world datasets demonstrate the
effectiveness and robustness of proposed A3T-GCN. The source code can be
visited at https://github.com/lehaifeng/T-GCN/A3T.
