A Volatilidade No Setor De Transporte Aéreo: Evidências Da Sua Ocorrência, Causas E Estratégias Para Enfrentá-La

Guerra, João Henrique Lopes; Pinto, Kleber Carlos Ribeiro; Miotto, Cláudio Luiz; Sá, Anderson Luiz Ribeiro de

doi:10.51923/repae.v7i3.257

Cited by 1 publication

(1 citation statement)

References 22 publications

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“…A fuzzy cognitive map of 39 concepts to analyse the links between them and estimate the causes and effects of volatility is drawn in [1]. Guerra et al conducted a literature review of volatility in air transport, identified factors that influence it and suggested strategies for addressing it [33]. However, they do not mention volatility as a measure of flight fluctuation.…”

Section: State Of the Artmentioning

confidence: 99%

Estimating the Volatility of Flights and Risk of Saturation of Airspaces in the European Core Area: A Methodological Proposal

Galarraga,

Abadie,

Standfuss

et al. 2023

Applied Sciences

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Despite 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.

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Section: State Of the Artmentioning

confidence: 99%