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

Author:

Galarraga Ibon123,Abadie Luis María12ORCID,Standfuss Thomas4,Ruiz de Gauna Itziar2,Goicoechea Nestor5ORCID

Affiliation:

1. Basque Centre for Climate Change (BC3), Edificio Sede 1, 1st Floor, Parque Científico UPV-EHU, Sarriena s/n, 48940 Leioa, Spain

2. Metroeconómica, Colón de Larreátegui 26, 48009 Bilbao, Spain

3. Campus of Leioa, Universidad del País Vasco-Euskal Herriko Unibertsitatea (UPV-EHU), Sarriena s/n, 48940 Leioa, Spain

4. Institute of Logistics and Aviation, Technische Universität Dresden, Gerhart-Potthoff-Bau (POT), Room 164, Hettnerstraβe 1-3, 01069 Dresden, Germany

5. Escuela de Ingeniería de Bilbao, Universidad del País Vasco-Euskal Herriko Unibertsitatea (UPV-EHU), Ingeniero Torres Quevedo Plaza 1, 48013 Bilbao, Spain

Abstract

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.

Publisher

MDPI AG

Subject

Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science

Reference43 articles.

1. Standfuss, T., Whittome, M., and Ruiz-Gauna, I. (2021). Electronic Navigation Research Institute Air Management and Systems IV, Springer. Lecture Notes in Electrical Engineering.

2. FABEC (2022, October 20). Volatility in ATM: Cases, Challenges, Solutions. InterFAB Panel at World ATM Congress 2018—FABEC OPS Theatre 6 March 2018. Available online: https://www.fabec.eu/images/user-pics/pdf-downloads/fabec_brochure-volatility-low.pdf.

3. Standfuss, T., and Whittome, M. (2022, November 03). How to Cope with Demand Fluctuations—Resilience as the Solution for Volatile Traffic? InterFAB Research Workshop “Single European Sky and Resilience in ATM. Available online: https://www.researchgate.net/publication/363640607_How_to_cope_with_Demand_Fluctuations_-_Resilience_as_the_Solution_for_Volatile_Traffic.

4. Impact of coronavirus (COVID-19) pandemic on air transport mobility, energy, and environment: A case study;Nizetic;Int. J. Energy Res.,2020

5. Passenger, airline, and policy responses to the COVID-19 crisis: The case of South Korea;Kim;J. Air Transp. Manag.,2022

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