DOI: https://doi.org/10.15368/theses.2013.78
Available at: https://digitalcommons.calpoly.edu/theses/1039
Date of Award
6-2013
Degree Name
MS in Electrical Engineering
Department/Program
Electrical Engineering
Advisor
Xiao-Hua (Helen) Yu
Abstract
Traffic signal control with swam intelligence ant colony optimization
Pang-shi Shih
Ant colony optimization (ACO) is a meta-heuristic based on the indirect communication of a colony of artificial ants mediated by pheromone trails with collaboration and knowledge-sharing mechanism during their food-seeking process. ACO has been successfully applied to solve many NP-hard combinational optimization problems such as travel salesman problem, quadratic problem, just to name a few. In this research, we apply the ACO algorithm to the traffic signal control in order to minimize the user delay at a traffic intersection. Simulation results from our computational experiments indicate that ACO provides better performance during high traffic demand, compared to the conventional Fully Actuated Control (FAC).
Keywords: Ant colony optimization (ACO), meta-heuristic, the traffic signal control, user delay