High-Order Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting

Traffic forecasting is a challenging task, due to the complicated spatial dependencies on roadway networks and the time-varying traffic patterns. To address this challenge, the authors learn the traffic network as a graph and propose a novel deep learning framework, High-Order Graph Convolutional Long Short-Term Memory Neural Network (HGC-LSTM), to learn the interactions between links in the traffic network and forecast the network-wide traffic state. The authors define the high-order traffic graph convolution based on the physical network topology. The proposed framework employs L1-norms on the graph convolution weights and L2-norms on the graph convolution features to identify the most influential links in the traffic network. The authors propose a novel Real-Time Branching Learning (RTBL) algorithm for the HGC-LSTM framework to accelerate the training process for spatio-temporal data. Experiments show that the authors HGC-LSTM network is able to capture the complex spatio-temporal dependencies efficiently present in a vehicle traffic network and consistently outperforms state-of-the-art baseline methods on two heterogeneous real-world traffic datasets. The visualization of graph convolution weights shows that the proposed framework can accurately recognize the most influential roadway segments in real-world traffic networks.

  • Supplemental Notes:
    • This paper was sponsored by TRB committee ABJ70 Standing Committee on Artificial Intelligence and Advanced Computing Applications.
  • Corporate Authors:

    Transportation Research Board

  • Authors:
  • Conference:
  • Date: 2019


  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; References; Tables;
  • Pagination: 6p

Subject/Index Terms

Filing Info

  • Accession Number: 01698033
  • Record Type: Publication
  • Report/Paper Numbers: 19-05236
  • Files: TRIS, TRB, ATRI
  • Created Date: Dec 7 2018 9:44AM