Intelligent testing of traffic light programs: Validation in smart mobility scenarios

Abstract

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 C O 2 emissions, in which we have obtained a reduction of 126.99% compared with the experts' solutions.