SmartSubway: um sistema colaborativo para apoiar o estudo da eficiência energética em trens urbanos no contexto de cidades inteligentes

• Ano de defesa: 2018
• Autor(a) principal: Queiroz, Mayrton Dias de
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal da Paraíba; Brasil; Informática; Programa de Pós-Graduação em Informática; UFPB
• Palavras-chave em Português: Sistemas colaborativos; Algoritmos genéticos; Eficiência energética; Transportes inteligentes; Cidades inteligentes; Collaborative systems; Genetic algorithms; Energy efficiency; Smart transportation; Smart cities; CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: https://repositorio.ufpb.br/jspui/handle/123456789/18404

With the increase in population, it becomes increasingly necessary to develop solutions that reduce the impacts generated by the increase in the consumption of natural resources. The adoption of electric transports, such as urban trains, is an alternative for the reduction of polluting gases in the atmosphere and the reduction in the occupation of urban space. However, with the use of electric trains, comes the problem of energy expenditure, which needs sustainable solutions. This work aims to create a generic method to support specialists in a collaborative way, in search of a speed profile that reduces energy costs. This approach is based on Genetic Algorithms from Artificial Intelligence, where experts can enter information about the domain, collaborating with each other, and in order to obtain real advantages with the use of this well-known and proven efficient meta-heuristic used for optimization problems. In order to obtain a proof of concept, a collaborative system, called SmartSubway, was developed. To validate the system, a case study is considered, where trajectory data is captured, and analyzes are performed through experiments with different configurations of the genetic algorithms. As results, the systems indicates the speed profiles with lower energy costs. Bringing environmental, economic and comfort benefits in urban mobility, in the context of solutions for Smart Cities.