Pronóstico pospandemia de tráfico aéreo. Caso de Colombia

La planificación aeroportuaria, y por lo tanto el desarrollo de las infraestructuras aéreas, depende en gran medida de los niveles de demanda que se prevén para el futuro. Para planificar las inversiones en infraestructura de un sistema aeroportuario y poder satisfacer las necesidades futuras, es es...

Full description

Autores:
Nagera Acosta, Ana Leonilde
Lemus Franco, Exmelin Hamid
Tipo de recurso:
Masters Thesis
Fecha de publicación:
2022
Institución:
Universidad Santo Tomás
Repositorio:
Repositorio Institucional USTA
Idioma:
spa
OAI Identifier:
oai:repository.usta.edu.co:11634/48350
Acceso en línea:
http://hdl.handle.net/11634/48350
Palabra clave:
airport
air transport
forecast
demand
Bayesian Structural Time Series
Ingeniería Civil
Aeroportuaria
Infraestructura-Áereas
Pasajeros
aeropuerto
transporte aéreo
pronóstico
demanda
series de tiempo estructural Bayesiano.
Rights
openAccess
License
Atribución-NoComercial-SinDerivadas 2.5 Colombia
id SANTTOMAS2_52bbe818b55184ce4898a029563a6ab0
oai_identifier_str oai:repository.usta.edu.co:11634/48350
network_acronym_str SANTTOMAS2
network_name_str Repositorio Institucional USTA
repository_id_str
dc.title.spa.fl_str_mv Pronóstico pospandemia de tráfico aéreo. Caso de Colombia
title Pronóstico pospandemia de tráfico aéreo. Caso de Colombia
spellingShingle Pronóstico pospandemia de tráfico aéreo. Caso de Colombia
airport
air transport
forecast
demand
Bayesian Structural Time Series
Ingeniería Civil
Aeroportuaria
Infraestructura-Áereas
Pasajeros
aeropuerto
transporte aéreo
pronóstico
demanda
series de tiempo estructural Bayesiano.
title_short Pronóstico pospandemia de tráfico aéreo. Caso de Colombia
title_full Pronóstico pospandemia de tráfico aéreo. Caso de Colombia
title_fullStr Pronóstico pospandemia de tráfico aéreo. Caso de Colombia
title_full_unstemmed Pronóstico pospandemia de tráfico aéreo. Caso de Colombia
title_sort Pronóstico pospandemia de tráfico aéreo. Caso de Colombia
dc.creator.fl_str_mv Nagera Acosta, Ana Leonilde
Lemus Franco, Exmelin Hamid
dc.contributor.advisor.none.fl_str_mv Diaz Olariaga, Oscar Eduardo
Rodriguez Pinzon, Heivar Yesid
dc.contributor.author.none.fl_str_mv Nagera Acosta, Ana Leonilde
Lemus Franco, Exmelin Hamid
dc.contributor.orcid.spa.fl_str_mv https://orcid.org/0000-0002-4858-3677
https://orcid.org/0000-0002-9553-0455
dc.contributor.googlescholar.spa.fl_str_mv https://scholar.google.com/citations?hl=es&user=v4XBXJAAAAAJ
https://scholar.google.com/citations?hl=es&user=9gC738EAAAAJ
dc.contributor.cvlac.spa.fl_str_mv https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0001561684
https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0001256491
dc.contributor.corporatename.spa.fl_str_mv Universidad Santo Tomas
dc.subject.keyword.spa.fl_str_mv airport
air transport
forecast
demand
Bayesian Structural Time Series
topic airport
air transport
forecast
demand
Bayesian Structural Time Series
Ingeniería Civil
Aeroportuaria
Infraestructura-Áereas
Pasajeros
aeropuerto
transporte aéreo
pronóstico
demanda
series de tiempo estructural Bayesiano.
dc.subject.lemb.spa.fl_str_mv Ingeniería Civil
Aeroportuaria
Infraestructura-Áereas
Pasajeros
dc.subject.proposal.spa.fl_str_mv aeropuerto
transporte aéreo
pronóstico
demanda
series de tiempo estructural Bayesiano.
description La planificación aeroportuaria, y por lo tanto el desarrollo de las infraestructuras aéreas, depende en gran medida de los niveles de demanda que se prevén para el futuro. Para planificar las inversiones en infraestructura de un sistema aeroportuario y poder satisfacer las necesidades futuras, es esencial predecir el nivel y la distribución de la demanda, tanto de pasajeros como de carga aérea. En el presente trabajo de tesis se realizó un pronóstico, a medio-largo plazo (10 años), de la demanda de pasajeros y de carga aérea, aplicado a un caso de estudio concreto, Colombia, y en donde se tuvo en cuenta el impacto en el tráfico aéreo del periodo más severo de la pandemia del COVID-19, año 2020, y el periodo de transición a la pospandemia (2021). Para conseguir tal objetivo, y como planteamiento metodológico, se desarrolla un modelo del tipo Bayesian Structural Time Series (BSTS), diseñado para trabajar con datos de series temporales, y muy utilizado para la selección de características, la previsión de series temporales, la predicción inmediata, y la inferencia del impacto causal. De los resultados obtenidos se puede destacar dos aspectos relevantes, en primer lugar, que tanto la demanda como la tendencia de crecimiento de la misma se recuperará muy pronto (en solo un par de años), con respecto al año prepandemia-2019, en el caso de estudio analizado. Y, en segundo lugar, el modelo presenta valores MAPE muy aceptables (de entre 1% y 7%, según la variable a pronosticar) lo que convierte al método BSTS en una metodología alternativa viable para el cálculo de pronóstico de tráfico aéreo.
publishDate 2022
dc.date.accessioned.none.fl_str_mv 2022-12-14T13:38:34Z
dc.date.available.none.fl_str_mv 2022-12-14T13:38:34Z
dc.date.issued.none.fl_str_mv 2022-12-13
dc.type.local.spa.fl_str_mv Tesis de maestría
dc.type.version.none.fl_str_mv info:eu-repo/semantics/acceptedVersion
dc.type.coar.none.fl_str_mv http://purl.org/coar/resource_type/c_bdcc
dc.type.drive.none.fl_str_mv info:eu-repo/semantics/masterThesis
format http://purl.org/coar/resource_type/c_bdcc
status_str acceptedVersion
dc.identifier.citation.spa.fl_str_mv Nagera Acosta, A. L. y Lemus Franco, E. H. (2022). Pronóstico post pandemia del tráfico aéreo. Caso de Colombia. [Trabajo de Grado, Universidad Santo Tomás]. Repositorio Institucional.
dc.identifier.uri.none.fl_str_mv http://hdl.handle.net/11634/48350
dc.identifier.reponame.spa.fl_str_mv reponame:Repositorio Institucional Universidad Santo Tomás
dc.identifier.instname.spa.fl_str_mv instname:Universidad Santo Tomás
dc.identifier.repourl.spa.fl_str_mv repourl:https://repository.usta.edu.co
identifier_str_mv Nagera Acosta, A. L. y Lemus Franco, E. H. (2022). Pronóstico post pandemia del tráfico aéreo. Caso de Colombia. [Trabajo de Grado, Universidad Santo Tomás]. Repositorio Institucional.
reponame:Repositorio Institucional Universidad Santo Tomás
instname:Universidad Santo Tomás
repourl:https://repository.usta.edu.co
url http://hdl.handle.net/11634/48350
dc.language.iso.spa.fl_str_mv spa
language spa
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spelling Diaz Olariaga, Oscar EduardoRodriguez Pinzon, Heivar YesidNagera Acosta, Ana LeonildeLemus Franco, Exmelin Hamidhttps://orcid.org/0000-0002-4858-3677https://orcid.org/0000-0002-9553-0455https://scholar.google.com/citations?hl=es&user=v4XBXJAAAAAJhttps://scholar.google.com/citations?hl=es&user=9gC738EAAAAJhttps://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0001561684https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0001256491Universidad Santo Tomas2022-12-14T13:38:34Z2022-12-14T13:38:34Z2022-12-13Nagera Acosta, A. L. y Lemus Franco, E. H. (2022). Pronóstico post pandemia del tráfico aéreo. Caso de Colombia. [Trabajo de Grado, Universidad Santo Tomás]. Repositorio Institucional.http://hdl.handle.net/11634/48350reponame:Repositorio Institucional Universidad Santo Tomásinstname:Universidad Santo Tomásrepourl:https://repository.usta.edu.coLa planificación aeroportuaria, y por lo tanto el desarrollo de las infraestructuras aéreas, depende en gran medida de los niveles de demanda que se prevén para el futuro. Para planificar las inversiones en infraestructura de un sistema aeroportuario y poder satisfacer las necesidades futuras, es esencial predecir el nivel y la distribución de la demanda, tanto de pasajeros como de carga aérea. En el presente trabajo de tesis se realizó un pronóstico, a medio-largo plazo (10 años), de la demanda de pasajeros y de carga aérea, aplicado a un caso de estudio concreto, Colombia, y en donde se tuvo en cuenta el impacto en el tráfico aéreo del periodo más severo de la pandemia del COVID-19, año 2020, y el periodo de transición a la pospandemia (2021). Para conseguir tal objetivo, y como planteamiento metodológico, se desarrolla un modelo del tipo Bayesian Structural Time Series (BSTS), diseñado para trabajar con datos de series temporales, y muy utilizado para la selección de características, la previsión de series temporales, la predicción inmediata, y la inferencia del impacto causal. De los resultados obtenidos se puede destacar dos aspectos relevantes, en primer lugar, que tanto la demanda como la tendencia de crecimiento de la misma se recuperará muy pronto (en solo un par de años), con respecto al año prepandemia-2019, en el caso de estudio analizado. Y, en segundo lugar, el modelo presenta valores MAPE muy aceptables (de entre 1% y 7%, según la variable a pronosticar) lo que convierte al método BSTS en una metodología alternativa viable para el cálculo de pronóstico de tráfico aéreo.Airport planning, and therefore the development of air infrastructure, depends to a large extent on the levels of demand that are forecast for the future. To plan investments in infrastructure of an airport system and to be able to meet future needs, it is essential to predict the level and distribution of demand, both for passengers and air cargo. In this thesis work, a medium-long term forecast (10 years) of the demand for passengers and air cargo was made, applied to a specific case study, Colombia, and where the impact on the air traffic during the most severe period of the COVID-19 pandemic, 2020, and the post-pandemic transition period (2021). To achieve this objective, and as a methodological approach, a model of the Bayesian Structural Time Series (BSTS) type is developed, designed to work with time series data, and widely used for feature selection, time series forecasting, immediate, and the inference of the causal impact. From the results obtained, two relevant aspects can be highlighted, firstly, that both demand and its growth trend will recover very soon (in just a couple of years), compared to the pre-pandemic year-2019, in which analyzed case study. And, secondly, the model presents very acceptable MAPE values (between 1% and 7%, depending on the variable to be forecast), which makes the BSTS method a viable alternative methodology for calculating air traffic forecasts.Magíster en Infraestructura VialMaestríaapplication/pdfspaUniversidad Santo TomásMaestría Infraestructura VialFacultad de Ingeniería CivilAtribución-NoComercial-SinDerivadas 2.5 Colombiahttp://creativecommons.org/licenses/by-nc-nd/2.5/co/Abierto (Texto Completo)info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Pronóstico pospandemia de tráfico aéreo. Caso de Colombiaairportair transportforecastdemandBayesian Structural Time SeriesIngeniería CivilAeroportuariaInfraestructura-ÁereasPasajerosaeropuertotransporte aéreopronósticodemandaseries de tiempo estructural Bayesiano.Tesis de maestríainfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_bdccinfo:eu-repo/semantics/masterThesisCRAI-USTA BogotáAbed, S. Y.; Ba-Fail, A. 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DOI: 10.1016/j.tranpol.2021.01.013ORIGINAL2022ananageraexmelinlemus.pdf2022ananageraexmelinlemus.pdfTrabajo de gradoapplication/pdf677805https://repository.usta.edu.co/bitstream/11634/48350/1/2022ananageraexmelinlemus.pdfe892caca4cab64023181ce76022ad404MD51open accessCarta de aprobación Negera-Lemus (1).pdfCarta de aprobación Negera-Lemus (1).pdfCarta de aprobación facultadapplication/pdf586108https://repository.usta.edu.co/bitstream/11634/48350/2/Carta%20de%20aprobaci%c3%b3n%20Negera-Lemus%20%281%29.pdfe90b63cb3b8aca8a20786af4c0833a8cMD52metadata only accessCarta_autorizacion_archivo_autores_2022 (1).pdfCarta_autorizacion_archivo_autores_2022 (1).pdfCarta derechos de autores.application/pdf422685https://repository.usta.edu.co/bitstream/11634/48350/3/Carta_autorizacion_archivo_autores_2022%20%281%29.pdfe72b0debf1566347712b63efe89591d7MD53metadata only accessCC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8811https://repository.usta.edu.co/bitstream/11634/48350/4/license_rdf217700a34da79ed616c2feb68d4c5e06MD54open accessLICENSElicense.txtlicense.txttext/plain; charset=utf-8807https://repository.usta.edu.co/bitstream/11634/48350/5/license.txtaedeaf396fcd827b537c73d23464fc27MD55open accessTHUMBNAIL2022ananageraexmelinlemus.pdf.jpg2022ananageraexmelinlemus.pdf.jpgIM Thumbnailimage/jpeg5168https://repository.usta.edu.co/bitstream/11634/48350/6/2022ananageraexmelinlemus.pdf.jpge96ce5fbca79a9af11a0b423e5382510MD56open accessCarta de aprobación Negera-Lemus (1).pdf.jpgCarta de aprobación Negera-Lemus (1).pdf.jpgIM Thumbnailimage/jpeg7564https://repository.usta.edu.co/bitstream/11634/48350/7/Carta%20de%20aprobaci%c3%b3n%20Negera-Lemus%20%281%29.pdf.jpge7894e24b1e22f791b27de168aa41341MD57open accessCarta_autorizacion_archivo_autores_2022 (1).pdf.jpgCarta_autorizacion_archivo_autores_2022 (1).pdf.jpgIM Thumbnailimage/jpeg8171https://repository.usta.edu.co/bitstream/11634/48350/8/Carta_autorizacion_archivo_autores_2022%20%281%29.pdf.jpg01f36e8a58a5b34db2dc78f476ac8824MD58open access11634/48350oai:repository.usta.edu.co:11634/483502023-05-09 08:57:59.235open accessRepositorio Universidad Santo Tomásrepositorio@usantotomas.edu.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