Machine-Learning Methods Estimating Flights’ Hidden Parameters for the Prediction of KPIs
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, i...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Aerospace |
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| Online Access: | https://www.mdpi.com/2226-4310/11/11/937 |
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| author | George Vouros Ioannis Ioannidis Georgios Santipantakis Theodore Tranos Konstantinos Blekas Marc Melgosa Xavier Prats |
| author_facet | George Vouros Ioannis Ioannidis Georgios Santipantakis Theodore Tranos Konstantinos Blekas Marc Melgosa Xavier Prats |
| author_sort | George Vouros |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-dd80ff0973154d4eaf90fb62f4014e82 |
| institution | OA Journals |
| issn | 2226-4310 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Aerospace |
| spelling | doaj-art-dd80ff0973154d4eaf90fb62f4014e822025-08-20T01:53:48ZengMDPI AGAerospace2226-43102024-11-01111193710.3390/aerospace11110937Machine-Learning Methods Estimating Flights’ Hidden Parameters for the Prediction of KPIsGeorge Vouros0Ioannis Ioannidis1Georgios Santipantakis2Theodore Tranos3Konstantinos Blekas4Marc Melgosa5Xavier Prats6Department of Digital Systems, School of Information and Communication Technologies, University of Piraeus, 18534 Piraeus, GreeceDepartment of Digital Systems, School of Information and Communication Technologies, University of Piraeus, 18534 Piraeus, GreeceDepartment of Digital Systems, School of Information and Communication Technologies, University of Piraeus, 18534 Piraeus, GreeceDepartment of Computer Science and Engineering, Polytechnic School, University of Ioannina, 45110 Ioannina, GreeceDepartment of Computer Science and Engineering, Polytechnic School, University of Ioannina, 45110 Ioannina, GreeceDivision of Aerospace Engineering, Department of Physics, Universitat Politècnica de Catalunya (UPC BarcelonaTECH), c/Esteve Terradas 7, 08860 Castelldefels, SpainDivision of Aerospace Engineering, Department of Physics, Universitat Politècnica de Catalunya (UPC BarcelonaTECH), c/Esteve Terradas 7, 08860 Castelldefels, SpainComplex 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.https://www.mdpi.com/2226-4310/11/11/937predictionhidden parametersKPIstake-off weightcost indexdeep learning |
| spellingShingle | George Vouros Ioannis Ioannidis Georgios Santipantakis Theodore Tranos Konstantinos Blekas Marc Melgosa Xavier Prats Machine-Learning Methods Estimating Flights’ Hidden Parameters for the Prediction of KPIs Aerospace prediction hidden parameters KPIs take-off weight cost index deep learning |
| title | Machine-Learning Methods Estimating Flights’ Hidden Parameters for the Prediction of KPIs |
| title_full | Machine-Learning Methods Estimating Flights’ Hidden Parameters for the Prediction of KPIs |
| title_fullStr | Machine-Learning Methods Estimating Flights’ Hidden Parameters for the Prediction of KPIs |
| title_full_unstemmed | Machine-Learning Methods Estimating Flights’ Hidden Parameters for the Prediction of KPIs |
| title_short | Machine-Learning Methods Estimating Flights’ Hidden Parameters for the Prediction of KPIs |
| title_sort | machine learning methods estimating flights hidden parameters for the prediction of kpis |
| topic | prediction hidden parameters KPIs take-off weight cost index deep learning |
| url | https://www.mdpi.com/2226-4310/11/11/937 |
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