  Predicting large-scale transportation network traffic has become an important
and challenging topic in recent decades. Inspired by the domain knowledge of
motion prediction, in which the future motion of an object can be predicted
based on previous scenes, we propose a network grid representation method that
can retain the fine-scale structure of a transportation network. Network-wide
traffic speeds are converted into a series of static images and input into a
novel deep architecture, namely, spatiotemporal recurrent convolutional
networks (SRCNs), for traffic forecasting. The proposed SRCNs inherit the
advantages of deep convolutional neural networks (DCNNs) and long short-term
memory (LSTM) neural networks. The spatial dependencies of network-wide traffic
can be captured by DCNNs, and the temporal dynamics can be learned by LSTMs. An
experiment on a Beijing transportation network with 278 links demonstrates that
SRCNs outperform other deep learning-based algorithms in both short-term and
long-term traffic prediction.
