Pronóstico pospandemia de tráfico aéreo. Caso de Colombia

dc.contributorDiaz Olariaga, Oscar Eduardo
dc.contributorRodriguez Pinzon, Heivar Yesid
dc.contributorhttps://orcid.org/0000-0002-4858-3677
dc.contributorhttps://orcid.org/0000-0002-9553-0455
dc.contributorhttps://scholar.google.com/citations?hl=es&user=v4XBXJAAAAAJ
dc.contributorhttps://scholar.google.com/citations?hl=es&user=9gC738EAAAAJ
dc.contributorhttps://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0001561684
dc.contributorhttps://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0001256491
dc.contributorUniversidad Santo Tomas
dc.creatorNagera Acosta, Ana Leonilde
dc.creatorLemus Franco, Exmelin Hamid
dc.date.accessioned2022-12-14T13:38:34Z
dc.date.accessioned2023-06-12T16:26:17Z
dc.date.available2022-12-14T13:38:34Z
dc.date.available2023-06-12T16:26:17Z
dc.date.created2022-12-14T13:38:34Z
dc.date.issued2022-12-13
dc.identifierNagera 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.identifierhttp://hdl.handle.net/11634/48350
dc.identifierreponame:Repositorio Institucional Universidad Santo Tomás
dc.identifierinstname:Universidad Santo Tomás
dc.identifierrepourl:https://repository.usta.edu.co
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/6658674
dc.description.abstractAirport 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.
dc.languagespa
dc.publisherUniversidad Santo Tomás
dc.publisherMaestría Infraestructura Vial
dc.publisherFacultad de Ingeniería Civil
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dc.rightsAbierto (Texto Completo)
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightshttp://purl.org/coar/access_right/c_abf2
dc.rightsAtribución-NoComercial-SinDerivadas 2.5 Colombia
dc.titlePronóstico pospandemia de tráfico aéreo. Caso de Colombia


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