Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks

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.…

• CA
  China Association for Science and Technology

  Awards: 2016QNRC001, U1564212, 9172011, 51408019
• NN
  National Natural Science Foundation of China

  Awards: 2014BAG01B02, U1564212, 2016QNRC001, 51408019, 51308021, 9172011
• NS
  Natural Science Foundation of Beijing Municipality

  Award: 9172011
• BN
  Beijing Nova Program

  Award: z151100000315048