Deep Learning Based Congestion Prediction Using PROBE Trajectory Data

Within transportation operations and research, the prediction of traffic congestion for a large-scale road network is always a challenge but is very useful. Different from traditional model driven approaches, this paper demonstrated an innovative data-driven approach that can effectively predict network-wide traffic congestion in short and long-term time spans. Based on the sanitized probe trajectory data, this paper proposed a hybrid deep learning architecture that combined 3-dimensional convolutional networks (C3D) with convolutional neuron networks (CNNs) and recurrent neuron networks (RNNs), which is called CRC3D. The prediction result of the CRC3D is further compared with a variety of recurrent neural network architectures. It is illustrated that the proposed model was successful in inheriting the advantages of C3D and CNN-RNN; and it could well reflect the trend and regularity of the traffic state with high accuracy, which can be used for large-scale transport network congestion prediction competitively.


  • English

Media Info

  • Media Type: Web
  • Monograph Title: CICTP 2019: Transportation in China—Connecting the World

Subject/Index Terms

Filing Info

  • Accession Number: 01713631
  • Record Type: Publication
  • ISBN: 9780784482292
  • Files: TRIS, ASCE
  • Created Date: Jul 2 2019 3:08PM