• DocumentCode
    2876491
  • Title

    Traffic Signal Control with Swarm Intelligence

  • Author

    Renfrew, David ; Yu, Xiao-Hua

  • Author_Institution
    Dept. of Electr. Eng., California Polytech. State Univ., San Luis Obispo, CA, USA
  • Volume
    3
  • fYear
    2009
  • fDate
    14-16 Aug. 2009
  • Firstpage
    79
  • Lastpage
    83
  • Abstract
    Traffic signal control is an effective way to regulate traffic flow to avoid conflict and reduce congestion. The ACO (Ant Colony Optimization) algorithm is an optimization technique based on swarm intelligence. This research investigates the application of ACO to traffic signal control problem. The decentralized, collective, stochastic, and self-organization properties of this algorithm fit well with the nature of traffic networks. Computer simulation results show that this method outperforms the conventional fully actuated control, especially under the condition of high traffic demand.
  • Keywords
    cooperative systems; optimisation; road traffic; ant colony optimization; swarm intelligence; traffic signal control; Adaptive control; Ant colony optimization; Application software; Communication system traffic control; Computer simulation; Particle swarm optimization; Roads; Stochastic processes; Timing; Traffic control; Traffic signal control; ant colony algorithm;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Natural Computation, 2009. ICNC '09. Fifth International Conference on
  • Conference_Location
    Tianjin
  • Print_ISBN
    978-0-7695-3736-8
  • Type

    conf

  • DOI
    10.1109/ICNC.2009.653
  • Filename
    5366981