• DocumentCode
    1675101
  • Title

    Traffic state identification methods based on cloud computing model

  • Author

    Liu, Wei-ning ; Ma, Qing-Lu ; Sun, Di-hua ; Dan, Yu-Fang

  • Author_Institution
    Coll. of Comput. Sci., Chongqing Univ., Chongqing, China
  • fYear
    2010
  • Firstpage
    4671
  • Lastpage
    4676
  • Abstract
    In order to identify the traffic state of urban road network accurately, the traffic state identification methods based on cloud computing model are proposed. The macroscopic and microscopic characteristics of urban road network traffic flow are discussed in detail; and then the identification index systems are proposed. The traffic state identification models are proposed based on cloud computing theory. Numerical results of an arterial road network testified to the effectiveness of the proposed methods. The methods proposed can be applied to traffic state analysis on-line and to extract the traffic information in historical database to provide decision support for traffic management.
  • Keywords
    Internet; decision support systems; road traffic; arterial road network; cloud computing model; decision support; identification index systems; traffic management; traffic state identification methods; urban road network; Cloud computing; Clouds; Cognition; Computational modeling; Educational institutions; Numerical models; Roads; cloud computing; cloud mode; comprehensive identification; traffic information; traffic state; urban road network;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Control and Automation (WCICA), 2010 8th World Congress on
  • Conference_Location
    Jinan
  • Print_ISBN
    978-1-4244-6712-9
  • Type

    conf

  • DOI
    10.1109/WCICA.2010.5553967
  • Filename
    5553967