Use SUMO Simulator for the Determination of Light Times in Order to Reduce Pollution: A Case Study in the City Center of Rio Grande, Brazil

Use SUMO Simulator for the Determination of Light Times in Order to Reduce Pollution: A Case Study in the City Center of Rio Grande, Brazil

Míriam Blank Born (Universidade Federal do Rio Grande (FURG), Brazil), Diana Francisca Adamatti (Universidade Federal do Rio Grande (FURG), Brazil), Marilton Sanchotene de Aguiar (Universidade Federal de Pelotas (UFPel), Brazil) and Weslen Schiavon de Souza (Universidade Federal de Pelotas (UFPel), Brazil)
DOI: 10.4018/978-1-5225-1756-6.ch010
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Abstract

Nowadays, urban mobility and air quality issues are prominent, due to the heavy traffic of vehicles and the emission of pollutants dissipated in the atmosphere. In the literature, a model of optimal control of traffic lights using Genetic Algorithms (GA) has been proposed. These algorithms have been introduced in the context of control traffic. In order to search for possible solutions to the problems of traffic lights in major urban centers. Thus, the study of the dispersion of pollutants and Genetic Algorithms with simulations performed in Urban Mobility Simulator SUMO (Simulation of Urban Mobility), seek satisfactory solutions to such problems. The AG uses the crossing of chromosomes, in this case the times of the traffic lights, featuring the finest green light times and the sum of each of the pollutants each simulation cycle. The simulations were performed and the results compared analyzes showed that the use of the genetic algorithm is very promising in this context.
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Background

At 60’s John Holland and students from the University of Michigan created Genetic Algorithms (GAs) The purpose of Holland was to study the phenomenon of “evolution” and play it somehow in computing (AGUIAR, 1998). According to (GOLDBERG, 1989) it was possible to get a computer version of the process of evolution and that it would be able to solve similar problems to the evolution of characteristics.

According to Holland work, the system was composed of a bit string (0's and 1's), called individuals. These individuals evolved to find a better chromosome that meets a specific problem. The solution was found automatically and unsupervised way and the information was provided in the settings of each chromosome.

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