Forecasting road traffic speeds by considering area-wide spatio-temporal dependencies based on a graph convolutional neural network (GCN)

The traffic state in an urban transportation network is determined via spatio-temporal traffic propagation. In early traffic forecasting studies, time-series models were adopted to accommodate autocorrelations between traffic states. The incorporation of spatial correlations into the forecasting of traffic states, however, involved a computational burden. Deep learning technologies were recently introduced to traffic forecasting in order to accommodate the spatio-temporal dependencies among traffic states. In the present study, the authors devised a novel graph-based neural network that expanded the existing graph convolutional neural network (GCN). The proposed model allowed the authors to differentiate the intensity of connecting to neighbor roads, unlike existing GCNs that give equal weight to each neighbor road. A plausible model architecture that mimicked real traffic propagation was established based on the graph convolution. The domain knowledge was efficiently incorporated into a neural network architecture. The present study also employed a generative adversarial framework to ensure that a forecasted traffic state could be as realistic as possible considering the joint probabilistic density of real traffic states. The forecasting performance of the proposed model surpassed that of the original GCN model, and the estimated adjacency matrices revealed the hidden nature of real traffic propagation.

• Record URL:
  https://doi.org/10.1016/j.trc.2020.02.013
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S0968090X19312434
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/0968090X
• Supplemental Notes:
  • © 2020 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Yu, Byeonghyeop
  • Lee, Yongjin
  • Sohn, Keemin
• Publication Date: 2020-5

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: pp 189-204
• Serial:
  • Transportation Research Part C: Emerging Technologies
  • Volume: 114
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0968-090X
  • Serial URL: http://www.sciencedirect.com/science/journal/0968090X

Subject/Index Terms

• TRT Terms: Forecasting; Graph theory; Highway traffic control; Neural networks; Traffic speed
• Subject Areas: Highways; Operations and Traffic Management; Planning and Forecasting;

Filing Info

• Accession Number: 01736096
• Record Type: Publication
• Files: TRIS
• Created Date: Apr 13 2020 9:37AM