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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/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

The Air Traffic Control (ATC) system suffers from an ever-increasing demand for aircraft, leading to capacity issues. For this reason, airspace is regulated by limiting the entry of aircraft into the airspace. Knowledge of these regulations before they occur would allow the ATC system to be aware of conflicting areas of the airspace, and to manage both its human and technological resources to lessen the effect of the expected regulations. Therefore, this paper develops a methodology in which the final result is a machine learning model that allows predicting capacity regulations. Predictions shall be based mainly on historical data, but also on the traffic situation at the time of the prediction. The results of tests of the model in a sector of Spanish airspace are satisfactory. In addition to testing the model results, special emphasis is placed on the explainability of the model. This explainability will help to understand the basis of the predictions and validate them from an operational point of view. The main conclusion after testing the model is that this model works well. Therefore, it is possible to predict when an ATC sector will be regulated or not based mainly on historical data.

Details

Title
Prediction of Capacity Regulations in Airspace Based on Timing and Air Traffic Situation
Author
Francisco Pérez Moreno  VIAFID ORCID Logo  ; Gómez Comendador, Víctor Fernando  VIAFID ORCID Logo  ; Raquel Delgado-Aguilera Jurado  VIAFID ORCID Logo  ; María Zamarreño Suárez  VIAFID ORCID Logo  ; Rosa María Arnaldo Valdés  VIAFID ORCID Logo 
First page
291
Publication year
2023
Publication date
2023
Publisher
MDPI AG
e-ISSN
22264310
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2791550974
Copyright
© 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/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.