Estimating entry counts and ATFM regulations during adverse weather conditions using machine learning

Aniel Jardines, Manuel Soler and García-Heras, Javier

Journal of Air Transport Management, 2021, vol. 95, issue C

Abstract: In recent years, convective weather has been the cause of significant delays in the European airspace. With climate experts anticipating the frequency and intensity of convective weather to increase in the future, it is necessary to find solutions that mitigate the impact of convective weather events on the airspace system. Analysis of historical air traffic and weather data will provide valuable insight on how to deal with disruptive convective events in the future. We propose a methodology for processing and integrating historic traffic and weather data to enable the use of machine learning algorithms to predict network performance during adverse weather. In this paper we develop regression and classification supervised learning algorithms to predict airspace performance characteristics such as entry count, number of flights impacted by weather regulations, and if a weather regulation is active. Examples using data from the Maastricht Upper Area Control Centre are presented with varying levels of predictive performance by the machine learning algorithms. Data sources include Demand Data Repository from EUROCONTROL and the Rapid Developing Thunderstorm product from EUMETSAT.

Keywords: Thunderstorms; Air traffic flow management; Data science; Machine learning; ATM data (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0969699721000922
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:jaitra:v:95:y:2021:i:c:s0969699721000922

DOI: 10.1016/j.jairtraman.2021.102109

Access Statistics for this article

Journal of Air Transport Management is currently edited by Anne Graham

More articles in Journal of Air Transport Management from Elsevier
Bibliographic data for series maintained by Catherine Liu ().