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.

提出了一种基于运动预测领域知识的网络网格表示方法，将网络整体交通速度转换为静态图像，并输入一种新的深度体系结构 —— 时空循环卷积网络 (SRCNs)，可以用于交通预测，实验证明其在短期和长期交通预测方面优于其他基于深度学习的算法。