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dc.contributor.authorGalarraga Gallastegui, Ibon
dc.contributor.authorAbadie, Luis María
dc.contributor.authorStandfuss, Thomas
dc.contributor.authorRuiz de Gauna, Itziar
dc.contributor.authorGoicoechea Larracoechea, Nestor
dc.date.accessioned2023-12-14T19:18:00Z
dc.date.available2023-12-14T19:18:00Z
dc.date.issued2023-11-22
dc.identifier.citationApplied Sciences 13(23) : (2023) // Article ID 12576es_ES
dc.identifier.issn2076-3417
dc.identifier.urihttp://hdl.handle.net/10810/63393
dc.description.abstractDespite having some fluctuations and the impact of the COVID-19 crisis, the demand for flights had a general growing trend for the past years. As the airspace is limited, efforts to better manage the total number of flights are noteworthy. In addition, volatility (i.e., unpredicted changes) in the number of flights has been observed to be increasing. Efforts to improve flight forecasting are thus necessary to improve air traffic efficiency and reduce costs. In this study, volatility in the number of flights is estimated based on past trends, and the outcomes are used to project future levels. This enables risk situations such as having to manage unexpectedly high numbers of flights to be predicted. The methodological approach analyses the Functional Airspace Block of Central Europe (FABEC). Based on the number of flights for 2015–2019, the following are calculated: historic mean, variance, volatility, 95th percentile, flights per hour and flights per day of the week in different time zones in six countries. Due to the nature of air traffic and the overdispersion observed, this study uses counting data models such as negative binomial regressions. This makes it possible to calculate risk measures including expected shortfall (ES) and value at risk (VaR), showing for each hour that the number of flights can exceed planned levels by a certain number. The study finds that in Germany and Belgium at 13:00 h there is a 5% worst-case possibility of having averages of 683 and 246 flights, respectively. The method proposed is useful for planning under uncertainties. It is conducive to efficient airspace management, so risk indicators help Air Navigation Service Providers (ANSPs) to plan for low-probability situations in which there may be large numbers of flights.es_ES
dc.description.sponsorshipThis research is supported by María de Maeztu Excellence Unit 2020–2027 Ref. CEX2021-001201-M, funded by MCIN/AEI/10.13039/501100011033. Further support is provided by the Spanish Ministry of Science, Innovation and Universities (MINECO) (Grant RTI-2018-093352-B-I00). Ibon Galarraga and Nestor Goicoechea are grateful for financial support from Research Group B at the University of the Basque Country (Ref. IT1777-22).es_ES
dc.language.isoenges_ES
dc.publisherMDPIes_ES
dc.relationinfo:eu-repo/grantAgreement/MINECO/RTI-2018-093352-B-I00es_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectair traffic managementes_ES
dc.subjectnumber of flightses_ES
dc.subjectuncertaintyes_ES
dc.subjectnegative binomial regressiones_ES
dc.subjectrisk measureses_ES
dc.subjectrisk of airspace saturationes_ES
dc.titleEstimating the Volatility of Flights and Risk of Saturation of Airspaces in the European Core Area: A Methodological Proposales_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.date.updated2023-12-08T15:10:56Z
dc.rights.holder© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).es_ES
dc.relation.publisherversionhttps://www.mdpi.com/2076-3417/13/23/12576es_ES
dc.identifier.doi10.3390/app132312576
dc.departamentoesExpresión gráfica y proyectos de ingeniería
dc.departamentoeuAdierazpen grafikoa eta ingeniaritzako proiektuak


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© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Except where otherwise noted, this item's license is described as © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).