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Machine-Learning Methods Estimating Flights' Hidden Parameters for the Prediction of KPIs.

Authors :
Vouros, George
Ioannidis, Ioannis
Santipantakis, Georgios
Tranos, Theodore
Blekas, Konstantinos
Melgosa, Marc
Prats, Xavier
Source :
Aerospace (MDPI Publishing); Nov2024, Vol. 11 Issue 11, p937, 18p
Publication Year :
2024

Abstract

Complex microscopic simulation models of strategic Air Traffic Management (ATM) performance assessment and decision-making are hindered by several factors. One of the most important is the existence of hidden parameters—such as aircraft take-off weight (TOW) and the selected cost index (CI)—which, if known, would allow for more effective performance modeling methodologies for assessing Key Performance Indicators (KPIs) at various levels of abstraction/detail, e.g., system-wide, or at the level of individual flights. This research proposes a data-driven methodology for the estimation of flights' hidden parameters combining mechanistic and advanced Artificial Intelligence/Machine Learning (AI/ML) models. Aiming at microsimulation models, our goal is to study the effect of these estimations on the prediction of flights' KPIs. In so doing, we propose a novel methodology according to which data-driven methods are trained given optimal trajectories (produced by mechanistic models) corresponding to known hidden parameter values, with the aim of predicting hidden parameters' values of unseen trajectories. The results show that estimations of hidden parameters support the accurate prediction of KPIs regarding the efficiency of flights: fuel consumption, gate-to-gate time and distance flown. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
22264310
Volume :
11
Issue :
11
Database :
Complementary Index
Journal :
Aerospace (MDPI Publishing)
Publication Type :
Academic Journal
Accession number :
181163312
Full Text :
https://doi.org/10.3390/aerospace11110937