Nano Technology

2009 Fifth International Conference on Natural Computation

Traffic Signal Control with Swarm Intelligence

David Renfrew, Xiao-Hua Yu Department of Electrical Engineering, California Polytechnic State University San Luis Obispo, CA 93407, USA Abstract

Traffic signal control is an effective way to regulate traffic flow to avoid conflict and reduce congestion. The ACO (Ant Colony) 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. the best pre-calculated off-line timing plan based the current traffic conditions on the road. Some latest developments on traffic signal control employ artificial intelligence technology, such as neural networks [4] and fuzzy logic [5]. Algorithms using Petri nets [6] and Markov decision control [7] have also been investigated in recent years. Ant colony algorithm is a meta-heuristic approach for solving computationally hard combinatorial optimization (CO) problems [8] [9] [11]. Inspired by the behavior of the ants in real world, ant colony algorithm is a multi-agent system, in which each single agent is called an artificial ant. It is one of the most successful examples of swarm intelligent systems and has been applied to solve many different types of problems, including the classical traveling salesman problem, path planning and network routing. In nature, when searching for food, real ants may wander randomly until they find food. As an ant returns to the colony with food, it deposits pheromone, a chemical used for communication. These pheromone trails guide other ants as they continue their search for food. As more pheromone is deposited,...