Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks
This work addresses traffic forecasting for transportation management, but it is incremental as it builds on existing deep learning methods.
The paper tackled traffic prediction in transportation networks by proposing spatiotemporal recurrent convolutional networks (SRCNs), which combine deep convolutional neural networks and LSTMs to capture spatial and temporal dependencies, and demonstrated that SRCNs outperform other deep learning-based algorithms on a Beijing network with 278 links.
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.