Multi objective particle swarm optimization algorithm for the design of efficient ATO speed profiles in metro lines
One of the strategies for the reduction of energy consumption in railways systems is to execute efficient drivings (eco-driving). This eco-driving is the speed profile that requires the minimum energy consumption without degrading commercial running times or passenger comfort. When the trains are eq...
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| Vydané v: | Engineering Applications of Artificial Intelligence Ročník 29; s. 43 - 53 |
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| Hlavní autori: | , , , , |
| Médium: | Journal Article |
| Jazyk: | English Japanese |
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Elsevier Ltd
01.03.2014
Elsevier BV |
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| ISSN: | 0952-1976, 1873-6769 |
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| Abstract | One of the strategies for the reduction of energy consumption in railways systems is to execute efficient drivings (eco-driving). This eco-driving is the speed profile that requires the minimum energy consumption without degrading commercial running times or passenger comfort. When the trains are equipped with Automatic Train Operation systems (ATO) additional difficulties are involved. Their particular features make it necessary to develop accurate models that optimize the combination of the ATO commands of each speed profile to be used by the traffic regulation system. These commands are transmitted to the train via encoded balises on the track with little channel capacity (bandwidth). Thus, only a few and discrete values of the commands can be sent and the solution space of every interstation is made up of a relatively small set of speed profiles. However, the new state-of-the-art of signalling technologies permit a better bandwidth resulting in an exponential solution space. This calls for new methods for the optimal design of the ATO speed profiles without an exhaustive simulation of all the combinations. A MOPSO algorithm (Multi Objective Particle Swarm Optimization) to obtain the consumption/time Pareto front based on the simulation of a train with a real ATO system is proposed. The algorithm is able even to take into account only the comfortable speed profiles of the solution space. The fitness of the Pareto front is verified by comparing it with a NSGA-II algorithm (non-dominated sorting genetic algorithm II) and with the real Pareto front. Further, it has been used to obtain the optimal speed profiles in a real line of the Madrid Underground. |
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| AbstractList | One of the strategies for the reduction of energy consumption in railways systems is to execute efficient drivings (eco-driving). This eco-driving is the speed profile that requires the minimum energy consumption without degrading commercial running times or passenger comfort. When the trains are equipped with Automatic Train Operation systems (ATO) additional difficulties are involved. Their particular features make it necessary to develop accurate models that optimize the combination of the ATO commands of each speed profile to be used by the traffic regulation system. These commands are transmitted to the train via encoded balises on the track with little channel capacity (bandwidth). Thus, only a few and discrete values of the commands can be sent and the solution space of every interstation is made up of a relatively small set of speed profiles. However, the new state-of-the-art of signalling technologies permit a better bandwidth resulting in an exponential solution space. This calls for new methods for the optimal design of the ATO speed profiles without an exhaustive simulation of all the combinations. A MOPSO algorithm (Multi Objective Particle Swarm Optimization) to obtain the consumption/time Pareto front based on the simulation of a train with a real ATO system is proposed. The algorithm is able even to take into account only the comfortable speed profiles of the solution space. The fitness of the Pareto front is verified by comparing it with a NSGA-II algorithm (non-dominated sorting genetic algorithm II) and with the real Pareto front. Further, it has been used to obtain the optimal speed profiles in a real line of the Madrid Underground. |
| Author | Cucala, Asunción P. Fernández, Adrián Domínguez, María Fernández-Cardador, Antonio Gonsalves, Tad |
| Author_xml | – sequence: 1 givenname: María surname: Domínguez fullname: Domínguez, María organization: Institute for Research in Technology, ICAI - School of Engineering, Comillas Pontifical University, 23 Alberto Aguilera Street, Madrid 28015, Spain – sequence: 2 givenname: Antonio surname: Fernández-Cardador fullname: Fernández-Cardador, Antonio email: antonio.fernandez@iit.upcomillas.es organization: Institute for Research in Technology, ICAI - School of Engineering, Comillas Pontifical University, 23 Alberto Aguilera Street, Madrid 28015, Spain – sequence: 3 givenname: Asunción P. surname: Cucala fullname: Cucala, Asunción P. organization: Institute for Research in Technology, ICAI - School of Engineering, Comillas Pontifical University, 23 Alberto Aguilera Street, Madrid 28015, Spain – sequence: 4 givenname: Tad surname: Gonsalves fullname: Gonsalves, Tad organization: Department of Information & Communication Sciences, Sophia University, 7-1 Kioi-cho, Chiyoda-ku, Tokyo 102-8554, Japan – sequence: 5 givenname: Adrián surname: Fernández fullname: Fernández, Adrián organization: Institute for Research in Technology, ICAI - School of Engineering, Comillas Pontifical University, 23 Alberto Aguilera Street, Madrid 28015, Spain |
| BackLink | https://cir.nii.ac.jp/crid/1874242817712390784$$DView record in CiNii |
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| Snippet | One of the strategies for the reduction of energy consumption in railways systems is to execute efficient drivings (eco-driving). This eco-driving is the speed... |
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| SubjectTerms | Algorithms ATO Commands Computer simulation Eco-driving Energy consumption Energy efficiency Metro MOPSO algorithm Pareto optimality Solution space Swarm intelligence Train simulation Trains |
| Title | Multi objective particle swarm optimization algorithm for the design of efficient ATO speed profiles in metro lines |
| URI | https://dx.doi.org/10.1016/j.engappai.2013.12.015 https://cir.nii.ac.jp/crid/1874242817712390784 https://www.proquest.com/docview/1530986161 |
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