Traffic Signal Optimization Using Ant Colony Algorithm

Abstract

Traffic signal control is an effective way to improve the efficiency of traffic networks and reduce users' delays. Ant Colony Optimization (ACO) is a meta-heuristic algorithm based on the behavior of ant colonies searching for food. ACO has successfully been employed to solve many complicated combinatorial optimization problems and its stochastic and decentralized nature fits well with traffic networks. This research investigates the application of the ant colony algorithm to minimize user delay at traffic intersections. Various ACO algorithms are discussed and a rolling horizon approach is also employed to achieve real-time adaptive control. Computer simulation results show that this new approach outperforms conventional fully actuated control, especially under the condition of high traffic demand