Modelo de operación automática de trenes basado en datos para sistemas ferroviarios sin sistemas de comunicación continua

ilustraciones, tablas

Autores:: Portilla Portillo, Estéfano Jesús

Tipo de recurso:

Fecha de publicación:: 2021

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: spa

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repository_id_str
dc.title.spa.fl_str_mv	Modelo de operación automática de trenes basado en datos para sistemas ferroviarios sin sistemas de comunicación continua
dc.title.translated.eng.fl_str_mv	Data based train automatic operation model for railway systems without continuos communication systems
title	Modelo de operación automática de trenes basado en datos para sistemas ferroviarios sin sistemas de comunicación continua
spellingShingle	Modelo de operación automática de trenes basado en datos para sistemas ferroviarios sin sistemas de comunicación continua 620 - Ingeniería y operaciones afines Railway transport Transporte ferroviario ATO Automatic train operation Data based train operation Data driven control Control with machine learning Operación automática de trenes Control con aprendizaje de máquina Operación de trenes basada en datos Control basado en datos
title_short	Modelo de operación automática de trenes basado en datos para sistemas ferroviarios sin sistemas de comunicación continua
title_full	Modelo de operación automática de trenes basado en datos para sistemas ferroviarios sin sistemas de comunicación continua
title_fullStr	Modelo de operación automática de trenes basado en datos para sistemas ferroviarios sin sistemas de comunicación continua
title_full_unstemmed	Modelo de operación automática de trenes basado en datos para sistemas ferroviarios sin sistemas de comunicación continua
title_sort	Modelo de operación automática de trenes basado en datos para sistemas ferroviarios sin sistemas de comunicación continua
dc.creator.fl_str_mv	Portilla Portillo, Estéfano Jesús
dc.contributor.advisor.none.fl_str_mv	Zapata Madrigal, German
dc.contributor.author.none.fl_str_mv	Portilla Portillo, Estéfano Jesús
dc.contributor.researchgroup.spa.fl_str_mv	Investigación en Teleinformática y Teleautomática (Grupo T&T)
dc.subject.ddc.spa.fl_str_mv	620 - Ingeniería y operaciones afines
topic	620 - Ingeniería y operaciones afines Railway transport Transporte ferroviario ATO Automatic train operation Data based train operation Data driven control Control with machine learning Operación automática de trenes Control con aprendizaje de máquina Operación de trenes basada en datos Control basado en datos
dc.subject.lemb.none.fl_str_mv	Railway transport Transporte ferroviario
dc.subject.proposal.eng.fl_str_mv	ATO Automatic train operation Data based train operation Data driven control Control with machine learning
dc.subject.proposal.spa.fl_str_mv	Operación automática de trenes Control con aprendizaje de máquina Operación de trenes basada en datos Control basado en datos
description	ilustraciones, tablas
publishDate	2021
dc.date.issued.none.fl_str_mv	2021
dc.date.accessioned.none.fl_str_mv	2022-02-11T16:35:11Z
dc.date.available.none.fl_str_mv	2022-02-11T16:35:11Z
dc.type.spa.fl_str_mv	Trabajo de grado - Maestría
dc.type.driver.spa.fl_str_mv	info:eu-repo/semantics/masterThesis
dc.type.version.spa.fl_str_mv	info:eu-repo/semantics/acceptedVersion
dc.type.content.spa.fl_str_mv	Text
dc.type.redcol.spa.fl_str_mv	http://purl.org/redcol/resource_type/TM
status_str	acceptedVersion
dc.identifier.uri.none.fl_str_mv	https://repositorio.unal.edu.co/handle/unal/80948
dc.identifier.instname.spa.fl_str_mv	Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv	https://repositorio.unal.edu.co/
url	https://repositorio.unal.edu.co/handle/unal/80948 https://repositorio.unal.edu.co/
identifier_str_mv	Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv	spa
language	spa
dc.relation.references.spa.fl_str_mv	J. Yin, T. Tang, L. Yang, J. Xun, Y. Huang, and Z. Gao, “Research and development of automatic train operation for railway transportation systems: A survey,” Transp. Res. Part C Emerg. Technol., vol. 85, pp. 548–572, 2017, doi: 10.1016/j.trc.2017.09.009. J. Yin, D. Chen, and Y. Li, “Smart train operation algorithms based on expert knowledge and ensemble CART for the electric locomotive,” Knowledge-Based Syst., vol. 92, pp. 78–91, 2016, doi: 10.1016/j.knosys.2015.10.016. C.-Y. Zhang, D. Chen, J. Yin, and L. Chen, “A flexible and robust train operation model based on expert knowledge and online adjustment,” Int. J. Wavelets, Multiresolution Inf. Process., vol. 15, no. 03, p. 1750023, 2017, doi: 10.1142/s0219691317500230. Y. Wang, M. Zhang, J. Ma, and X. Zhou, “Survey on Driverless Train Operation for Urban Rail Transit Systems,” Urban Rail Transit, vol. 2, no. 3–4, pp. 106–113, 2016, doi: 10.1007/s40864-016-0047-8. C. Y. Zhang, D. Chen, J. Yin, and L. Chen, “Data-driven train operation models based on data mining and driving experience for the diesel-electric locomotive,” Adv. Eng. Informatics, vol. 30, no. 3, pp. 553–563, 2016, doi: 10.1016/j.aei.2016.07.004. Y. J.a, C. D.a, and L. L.b, “Intelligent train operation algorithms for subway by expert system and reinforcement learning,” IEEE Trans. Intell. Transp. Syst., vol. 15, no. 6, pp. 2561–2571, 2014, doi: 10.1109/TITS.2014.2320757. S. Clark, “A historical overview of railway signalling & control (or `from Bobbies to Balises’),” in IET 13th Professional Development Course on Electric Traction Systems, 2014, pp. 4 (18 .)-4 (18 .), doi: 10.1049/cp.2014.1433. S. Morar, “Evolution of communication based train control worldwide,” IET Semin. Dig., vol. 2012, no. 14926, pp. 218–226, 2012, doi: 10.1049/ic.2012.0054. 82 Título de la tesis o trabajo de investigación G. M. Scheepmaker, H. Y. Willeboordse, J. H. Hoogenraad, R. S. Luijt, and R. M. P. Goverde, “Comparing train driving strategies on multiple key performance indicators,” J. Rail Transp. Plan. Manag., vol. 13, no. November 2019, p. 100163, 2020, doi: 10.1016/j.jrtpm.2019.100163. Y. Wang, N. Bin, B. Ton van den, and D. S. Bart, Optimal Trajectory Planning and Train Scheduling for Urban Rail Transit Systems. 2016. A. Naweed and G. Balakrishnan, “Understanding the visual skills and strategies of train drivers in the urban rail environment,” Work, vol. 47, no. 3, pp. 339–352, 2014, doi: 10.3233/WOR-131705. “IEC 62290-1 Railway applications: urban guided transport management and command/control systems. Part 1: system principles and fundamental concepts.,” Int. Electrotech. Comm., 2014. H. rong Dong, S. gen Gao, B. Ning, and L. Li, “Extended fuzzy logic controller for high speed train,” Neural Comput. Appl., vol. 22, no. 2, pp. 321–328, 2013, doi: 10.1007/s00521-011-0681-8. D. Gong and G. 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dc.publisher.spa.fl_str_mv	Universidad Nacional de Colombia
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dc.publisher.department.spa.fl_str_mv	Departamento de Ingeniería Eléctrica y Automática
dc.publisher.faculty.spa.fl_str_mv	Facultad de Minas
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spelling	Reconocimiento 4.0 Internacionalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Zapata Madrigal, Germanb877cf12ce65a7bb4d20614c97057b0a600Portilla Portillo, Estéfano Jesús2f7f77b2470b0328004be5154f9a8f8aInvestigación en Teleinformática y Teleautomática (Grupo T&T)2022-02-11T16:35:11Z2022-02-11T16:35:11Z2021https://repositorio.unal.edu.co/handle/unal/80948Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, tablasEl presente trabajo presenta la formulación y evaluación de un modelo de operación automática de trenes basado en datos para sistemas ferroviarios con sistema de control basado en comunicaciones (CBTC por sus siglas en inglés) y sin sistemas de comunicación de alta frecuencia. El modelo propuesto se enmarca en la operación automática de trenes con perfiles de velocidad calculados fuera de línea e integra una corrección de salida de control basada en reglas heurísticas. Los perfiles de velocidad usados por el modelo propuesto se denominan perfiles de velocidad condicionados, estos se obtienen a partir de un modelo de procesamiento de información, el cual usa los datos históricos de viaje de conducción manual y el conocimiento de los conductores experimentados. El modelo de procesamiento de información integra aprendizaje profundo y aprendizaje reforzado para obtener perfiles de velocidad sujetos a las condiciones reales del sistema ferroviario, evitando la necesidad del modelado de las dinámicas complejas de la conducción de trenes. Para la obtención de la corrección heurística, se propone usar el conocimiento de los conductores experimentados, el cual es consolidado en una serie de reglas heurísticas que se integran al algoritmo del modelo de operación automática de trenes. El modelo de operación automática de trenes propuesto en este trabajo es desarrollado e implementado para un sistema ferroviario que no cuenta con un sistema de comunicación de alta frecuencia y que opera con conducción manual. El desempeño del modelo se evalúa usando indicadores de confort, seguridad, consumo energético y puntualidad. (Texto tomado de la fuente)This study presents the drafting and assessment of a data based automatic train operation model for railways with communication-based train control (CBTC) and without high frequency communication systems. The model proposed is framed in automatic train operation with speed profiles calculated offline and it integrates a control output correction based on heuristic rules. The speed profiles used by the proposed model are called conditioned speed profiles. These are obtained from an information processing model which uses historical data from manual driving and knowledge from experienced drivers. The information processing model integrates deep and reinforcement learning to obtain speed profiles subject to real railway system conditions, avoiding the need for modeling the complex dynamics of train driving. To obtain heuristic correction, it is proposed the use of experienced drivers’ knowledge which is consolidated in a series of heuristic rules that are integrated into the algorithm of the proposed train operation model. The automatic train operation model proposed in this study is developed and implemented for a railway system that does not have a high-frequency communication system and that operates with manual driving. The model performance is evaluated using comfort, safety, energy consumption, and punctuality indicators.MaestríaMagister en ingeniería - Automatización IndustrialAutomatización integrada inteligenteÁrea Curricular de Ingeniería Eléctrica e Ingeniería de Controlxvi, 86 páginasapplication/pdfspaUniversidad Nacional de ColombiaMedellín - Minas - Maestría en Ingeniería - Automatización IndustrialDepartamento de Ingeniería Eléctrica y AutomáticaFacultad de MinasMedellín, ColombiaUniversidad Nacional de Colombia - Sede Medellín620 - Ingeniería y operaciones afinesRailway transportTransporte ferroviarioATOAutomatic train operationData based train operationData driven controlControl with machine learningOperación automática de trenesControl con aprendizaje de máquinaOperación de trenes basada en datosControl basado en datosModelo de operación automática de trenes basado en datos para sistemas ferroviarios sin sistemas de comunicación continuaData based train automatic operation model for railway systems without continuos communication systemsTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMMedellín, ColombiaJ. Yin, T. Tang, L. Yang, J. Xun, Y. Huang, and Z. Gao, “Research and development of automatic train operation for railway transportation systems: A survey,” Transp. Res. Part C Emerg. Technol., vol. 85, pp. 548–572, 2017, doi: 10.1016/j.trc.2017.09.009.J. Yin, D. Chen, and Y. Li, “Smart train operation algorithms based on expert knowledge and ensemble CART for the electric locomotive,” Knowledge-Based Syst., vol. 92, pp. 78–91, 2016, doi: 10.1016/j.knosys.2015.10.016.C.-Y. Zhang, D. Chen, J. Yin, and L. Chen, “A flexible and robust train operation model based on expert knowledge and online adjustment,” Int. J. Wavelets, Multiresolution Inf. Process., vol. 15, no. 03, p. 1750023, 2017, doi: 10.1142/s0219691317500230.Y. Wang, M. Zhang, J. Ma, and X. Zhou, “Survey on Driverless Train Operation for Urban Rail Transit Systems,” Urban Rail Transit, vol. 2, no. 3–4, pp. 106–113, 2016, doi: 10.1007/s40864-016-0047-8.C. Y. Zhang, D. Chen, J. Yin, and L. Chen, “Data-driven train operation models based on data mining and driving experience for the diesel-electric locomotive,” Adv. Eng. Informatics, vol. 30, no. 3, pp. 553–563, 2016, doi: 10.1016/j.aei.2016.07.004.Y. J.a, C. D.a, and L. L.b, “Intelligent train operation algorithms for subway by expert system and reinforcement learning,” IEEE Trans. Intell. Transp. Syst., vol. 15, no. 6, pp. 2561–2571, 2014, doi: 10.1109/TITS.2014.2320757.S. Clark, “A historical overview of railway signalling & control (or `from Bobbies to Balises’),” in IET 13th Professional Development Course on Electric Traction Systems, 2014, pp. 4 (18 .)-4 (18 .), doi: 10.1049/cp.2014.1433.S. Morar, “Evolution of communication based train control worldwide,” IET Semin. Dig., vol. 2012, no. 14926, pp. 218–226, 2012, doi: 10.1049/ic.2012.0054. 82 Título de la tesis o trabajo de investigaciónG. M. Scheepmaker, H. Y. Willeboordse, J. H. Hoogenraad, R. S. Luijt, and R. M. P. 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EVESURBIFBPUiBMQSBTRUNSRVRBUsONQSBHRU5FUkFMLiAqTEEgVEVTSVMgQSBQVUJMSUNBUiBERUJFIFNFUiBMQSBWRVJTScOTTiBGSU5BTCBBUFJPQkFEQS4gCgpBbCBoYWNlciBjbGljIGVuIGVsIHNpZ3VpZW50ZSBib3TDs24sIHVzdGVkIGluZGljYSBxdWUgZXN0w6EgZGUgYWN1ZXJkbyBjb24gZXN0b3MgdMOpcm1pbm9zLiBTaSB0aWVuZSBhbGd1bmEgZHVkYSBzb2JyZSBsYSBsaWNlbmNpYSwgcG9yIGZhdm9yLCBjb250YWN0ZSBjb24gZWwgYWRtaW5pc3RyYWRvciBkZWwgc2lzdGVtYS4KClVOSVZFUlNJREFEIE5BQ0lPTkFMIERFIENPTE9NQklBIC0gw5psdGltYSBtb2RpZmljYWNpw7NuIDE5LzEwLzIwMjEK

Modelo de operación automática de trenes basado en datos para sistemas ferroviarios sin sistemas de comunicación continua

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