• DocumentCode
    2901580
  • Title

    Modeling and clustering network-level urban traffic status based on traffic flow assignment ratios

  • Author

    Li Qu ; Jianming Hu ; Yi Zhang

  • Author_Institution
    Dept. of Autom., Tsinghua Univ., Beijing, China
  • fYear
    2010
  • fDate
    19-22 Sept. 2010
  • Firstpage
    551
  • Lastpage
    556
  • Abstract
    The detected traffic data for single point or link cannot satisfy the needs for network-level traffic status information with the rapid development of the traffic control and guidance systems. This paper proposed a modeling and clustering method for network-level urban traffic status based on the dynamic traffic flow assignment ratios. The traffic assignment ratio matrix model integrates traffic status, topology and relation between links, with the dynamic traffic assignment ratios estimated by Linear Programming. The network-level traffic status is clustered by Self-Organizing Map and the typical patterns are discovered. The experiment proves the efficiency and applicability of this method for network-level traffic status modeling and analyzing.
  • Keywords
    linear programming; pattern clustering; road traffic; self-organising feature maps; traffic information systems; dynamic traffic assignment ratios; guidance systems; linear programming; network-level traffic status modeling; network-level urban traffic status clustering; self-organizing map; traffic assignment ratio matrix model; traffic control; traffic data detection; traffic flow assignment ratios; Analytical models; Estimation; Hidden Markov models; Network topology; Neurons; Optimization; Topology;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Transportation Systems (ITSC), 2010 13th International IEEE Conference on
  • Conference_Location
    Funchal
  • ISSN
    2153-0009
  • Print_ISBN
    978-1-4244-7657-2
  • Type

    conf

  • DOI
    10.1109/ITSC.2010.5625105
  • Filename
    5625105