Analysis of Large-scale Traffic Dynamics using Non-negative Tensor Factorization

This paper presents work on clustering and prediction of temporal dynamics of global congestion configurations in large-scale road networks. Instead of looking into temporal traffic state variation of individual links, or of small areas, we focus on spatial congestion configurations of the whole network. In our work, we aim at describing the typical temporal dynamic patterns of this network-level traffic state and achieving long-term prediction of the large-scale traffic dynamics, in a unified data-mining framework. To this end, the authors formulate this joint task using Non-negative Tensor Factorization, which has been shown to be a useful decomposition tools for multivariate data sequences. Clustering and prediction are performed based on the compact tensor factorization results. Experiments on large-scale simulated data illustrate the interest of our method with promising results for long-term forecast of traffic evolution.

  • Supplemental Notes:
    • Abstract reprinted with permission from Intelligent Transportation Society of America.
  • Corporate Authors:

    ITS America

    1100 17th Street, NW, 12th Floor
    Washington, DC  United States  20036
  • Authors:
    • Han, Yufei
    • Moutarde, Fabien
  • Conference:
  • Publication Date: 2012


  • English

Media Info

  • Media Type: Digital/other
  • Features: CD-ROM; Figures; References; Tables;
  • Pagination: 12p
  • Monograph Title: 19th ITS World Congress, Vienna, Austria, 22 to 26 October 2012

Subject/Index Terms

Filing Info

  • Accession Number: 01499023
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Nov 21 2013 9:14AM