• DocumentCode
    3361977
  • Title

    Study of expressway traffic flow for off-ramp based on cellular automata

  • Author

    Zhang Hai-bo ; Liu Xiao-ming

  • Author_Institution
    North China Univ. of Technol., Beijing, China
  • fYear
    2010
  • fDate
    26-28 June 2010
  • Firstpage
    4756
  • Lastpage
    4760
  • Abstract
    The expressway traffic flow characteristics of off-ramp have an important impact on traffic congestion. By analyzing the vehicle car-following and lane-changing behavior, the cellular automata model of off-ramp area was built. When analyzing lane-changing behavior, the behavior of vehicle changing lane from main lane to off-ramp buffer lane was considered, and the information interaction process between the vehicle ready to changing lane and the rear vehicle on the target lane was considered, the new lane-changing rules were built. In the new lane-changing rules, free lane-changing and compulsory lane-changing were included. By analyzing the simulation results, when traffic flow density is lower, the behavior of vehicles in the main lane changing lane into off-ramp buffer lane has less effect on traffic capacity of the road section, but when traffic flow density rising to a certain extent, the behavior of vehicles in the main lane changing lane into off-ramp buffer lane will bring an adverse effect on traffic capacity of the road section.
  • Keywords
    automobiles; cellular automata; road traffic; cellular automata; expressway traffic flow; information interaction process; off-ramp buffer lane; rear vehicle; road traffic capacity; traffic congestion; vehicle car following behavior; vehicle lane changing behavior; Analytical models; Information analysis; Road vehicles; Traffic control; Virtual colonoscopy; cellular automata; compulsory lane-changing; lane-changing; off- ramp;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Mechanic Automation and Control Engineering (MACE), 2010 International Conference on
  • Conference_Location
    Wuhan
  • Print_ISBN
    978-1-4244-7737-1
  • Type

    conf

  • DOI
    10.1109/MACE.2010.5536369
  • Filename
    5536369