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
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;
Conference_Titel :
Natural Computation, 2009. ICNC '09. Fifth International Conference on
Conference_Location :
Tianjin
Print_ISBN :
978-0-7695-3736-8
DOI :
10.1109/ICNC.2009.653