@inproceedings{0d71aabb8b91422ca986e9deaf8029a6,
title = "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.",
keywords = "Ant colony algorithm, optimization, traffic signal control",
author = "David Renfrew and Yu, \{Xiao Hua\}",
year = "2012",
doi = "10.1109/IJCNN.2012.6252852",
language = "English",
isbn = "9781467314909",
series = "Proceedings of the International Joint Conference on Neural Networks",
booktitle = "2012 International Joint Conference on Neural Networks, IJCNN 2012",
note = "2012 Annual International Joint Conference on Neural Networks, IJCNN 2012, Part of the 2012 IEEE World Congress on Computational Intelligence, WCCI 2012 ; Conference date: 10-06-2012 Through 15-06-2012",
}