Multiple-objective optimization of traffic lightsusing a genetic algorithm and a microscopic traffic simulator
Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Given the demand for mobility in our society, the cost of building additionalinfrastructures and the increasing concerns about the sustainability of the trafficsystem, traffic managers have to come up with new tools to optimize the trafficconditions within the existing infrastructure. This study considered to optimizethe durations of the green light phases in order to improve several criteria such asthe ability of the network to deal with important demands or the total pollutantemissions.
Because the modeling of the problem is difficult and computationally demanding,a stochastic micro-simulator called ’Simulation of Urban MObility’ (SUMO) has been used with a stochastic optimization process, namely a Genetic Algorithm (GA).
The research objective of the study was to create a computational frameworkbased on the integration of SUMO and a Multi-Objective Genetic-Algorithm (MOGA).The proposed framework was demonstrated on a medium-size network correspondingto a part of the town of Rouen, France. This network is composed of 11 intersections,168 traffic lights and 40 possible turning movements. The network is monitored with20 sensors, spread over the network. The MOGA considered in this study is basedon NSGA-II. Several aspects have been investigated during the course of this thesis.
An initial study shows that the proposed MOGA is successful in optimizing thesignal control strategies for a medium-sized network within a reasonable amount oftime.
A second study has been conducted to optimize the demand-related model ofSUMO in order to ensure that the behavior in the simulated environment is close tothe real one. The study shows that a hybrid algorithm composed of a gradient searchalgorithm combined with a GA achieved a satisfactory behavior2 for a medium-sizenetwork within a reasonable time.
Place, publisher, year, edition, pages
Trafic optimozation, genetic algorithms, gradient optimization, trafic simulator.
IdentifiersURN: urn:nbn:se:kth:diva-168413OAI: oai:DiVA.org:kth-168413DiVA: diva2:816306