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Mathematical Problems in Engineering - Volume 2016 2016, Article ID 3871046, 19 pages -

Research ArticleDepartamento de Lenguajes y Ciencias de la Computación, Universidad de Málaga, E.T.S. Ingenieria Informatica, Bulevar Louis Pasteur 35, 29071 Málaga, Spain

Received 1 July 2015; Revised 24 December 2015; Accepted 12 January 2016

Academic Editor: Dan Simon

Copyright © 2016 Javier Ferrer et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


In smart cities, the use of intelligent automatic techniques to find efficient cycle programs of traffic lights is becoming an innovative front for traffic flow management. However, this automatic programming of traffic lights requires a validation process of the generated solutions, since they can affect the mobility and security of millions of citizens. In this paper, we propose a validation strategy based on genetic algorithms and feature models for the automatic generation of different traffic scenarios checking the robustness of traffic light cycle programs. We have concentrated on an extensive urban area in the city of Malaga in Spain, in which we validate a set of candidate cycle programs generated by means of four optimization algorithms: Particle Swarm Optimization for Traffic Lights, Differential Evolution for Traffic Lights, random search, and Sumo Cycle Program Generator. We can test the cycles of traffic lights considering the different states of the city, weather, congestion, driver expertise, vehicle’s features, and so forth, but prioritizing the most relevant scenarios among a large and varied set of them. The improvement achieved in solution quality is remarkable, especially for emissions, in which we have obtained a reduction of 126.99% compared with the experts’ solutions.

Autor: Javier Ferrer, José García-Nieto, Enrique Alba, and Francisco Chicano



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