A Framework to Support Experts in the Study of Energy Efficiency in Urban Trains
Authors: 
Mayrton D. de Queiroz, Ruan A. P. Palmeira, Felipe T. de Melo, Rodrigo G. Daniel, Ícaro T. de A. Rique, Augusto C. P. Guimarães, Marcelle B. Martins, Natasha C. Q. LinoAuthors Info & Claims
Article No.: 5, Pages 1 - 8
Abstract
In the context of Smart Cities, there are concerns regarding Urban Mobility, which consists of identifying alternatives for the reduction of traffic of individual vehicles, better occupation of urban space, among other aspects. An alternative is the adoption of electric trains. However, a problem concerning energy consumption arises. Thus, this work aims to propose a framework based on Genetic Algorithms (GAs), called SmartSubway, to assist specialists with insertion of domain information in the problem of energy efficiency in electric trains in order to identify energy efficient driving profiles. As proof of concept, a system inspired in GAs was implemented. To validate the system, the domain information of a real scenario was inserted, where it was possible to carry out six experiments and identify the ones that obtained the best results.
References
[1]
Ricardo Alexandre Afonso, W M Da Silva, GHRP Tomas, Kiev Gama, Alezy Oliveira, Alexandre Alvaro, and Vinicius Cardoso Garcia. 2013. Br-SCMM: Modelo Brasileiro de Maturidade para Cidades Inteligentes. In Simpósio Brasileiro De Sistemas De Informação.
[2]
Andre L L Aquino, Heitor S Ramos, Leonardo V Pereira, and Alejandro C Frery. 2015. Cidades Inteligentes, um Novo Paradigma da Sociedade do Conhecimento. Blucher Education Proceedings 1, 1 (2015), 165--178.
[3]
Herbertt BM Diniz, ECGF Silva, and K Gama. 2015. Uma Arquitetura de Referência para Plataforma de Crowdsensing em Smart Cities. In XI Brazilian Symposium on Information System. 26--29.
[4]
Ricardo Alexandre Afonso, Clóvis Holanda do Nascimento, Vinicius Cardoso Garcia, and Alexandre Álvaro. 2015. Smartcluster: Utilizando dados públicos para agrupar cidades inteligentes por domínios. In XI Simpósio Brasileiro de Sistemas de Informação (SBSI). 691--694.
[5]
David José Soares. 2012. Cidades Inteligentes: um novo arranjo para o desenvolvimento. In II Encontro da Faculdades de gestão e negócios.
[6]
Youneng Huang, Xiao Ma, Shuai Su, and Tao Tang. 2015. Optimization of train operation in multiple interstations with multi-population genetic algorithm. Energies 8, 12 (2015), 14311--14329.
[7]
DING Yong, LIU Haidong, BAI Yun, and ZHOU Fangming. 2011. A two-level optimization model and algorithm for energy-efficient urban train operation. Journal of Transportation Systems Engineering and Information Technology 11, 1 (2011), 96--101.
[8]
Shuai Su, Xiang Li, Tao Tang, and Ziyou Gao. 2013. A subway train timetable optimization approach based on energy-efficient operation strategy. IEEE Transactions on Intelligent Transportation Systems 14, 2 (2013), 883--893.
[9]
Xin Yang, Anthony Chen, Xiang Li, Bin Ning, and Tao Tang. 2015. An energyefficient scheduling approach to improve the utilization of regenerative energy for metro systems. Transportation Research Part C: Emerging Technologies 57 (2015), 13--29.
[10]
Hassan Abdulsalam Hamid, Gemma L Nicholson, Heather Douglas, Ning Zhao, and Clive Roberts. 2016. Investigation into train positioning systems for saving energy with optimised train trajectories. In Intelligent Rail Transportation (ICIRT), 2016 IEEE International Conference on. IEEE, 460--468.
[11]
C Sicre, AP Cucala, and Antonio Fernández-Cardador. 2014. Real time regulation of efficient driving of high speed trains based on a genetic algorithm and a fuzzy model of manual driving. Engineering Applications of Artificial Intelligence 29 (2014), 79--92.
[12]
Marcelle Batista Martins, Natasha Correia Queiroz Lino, and Abel Cavalcante Lima Filho. 2016. GeneticBee: A theoretic and evolutionary formulation for the energy efficiency problem of urban trains. In 2016 IEEE ANDESCON. IEEE, 1--4.
[13]
Xin Yang, Xiang Li, Bin Ning, and Tao Tang. 2016. A survey on energy-efficient train operation for urban rail transit. IEEE Transactions on Intelligent Transportation Systems 17, 1 (2016), 2--13.
[14]
Mayrton Queiroz, Marcelle Martins, Rodrigo Gonçalves Daniel, and Natasha Lino. 2016. Um estudo exploratório sobre o uso de algoritmos genéticos para o problema de eficiência energética em trens urbanos. In ENUCOMP 2016.
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- A Framework to Support Experts in the Study of Energy Efficiency in Urban Trains
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Published In
May 2019
623 pages
ISBN:9781450372374
DOI:10.1145/3330204
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Published: 20 May 2019
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- Conselho Nacional de Desenvolvimento Científico e Tecnológico
- Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
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SBSI'19
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