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oai:riuma.uma.es:10630/321022026-02-03T11:06:17Zcom_10630_2254col_10630_37953

00925njm 22002777a 4500 dc Ferrer-Urbano, Francisco Javier author García-Nieto, José Manuel author Alba-Torres, Enrique author Chicano-García, José-Francisco author 2016-02-08 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 ofMalaga (in Spain), in which we validate a set of candidate cycle programs generated bymeans of four optimization algorithms: Particle SwarmOptimization 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 CO2 emissions, in which we have obtained a reduction of 126.99% compared with the experts’ solutions. Ferrer, Javier, García-Nieto, José, Alba, Enrique, Chicano, Francisco, Intelligent Testing of Traffic Light Programs: Validation in Smart Mobility Scenarios, Mathematical Problems in Engineering, 2016, 3871046, 19 pages, 2016. https://doi.org/10.1155/2016/3871046 https://hdl.handle.net/10630/32102 10.1155/2016/3871046 Tráfico - Regulación Optimización matemática Intelligent Testing of Traffic Light Programs: Validation in Smart Mobility Scenarios.