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Article

An Adaptive Framework for Optimization and Prediction of Air Traffic Management (Sub-)Systems with Machine Learning

by 1,*,† and 2,†
1
Institute of Informatics, Freiberg University of Mining and Technology, 09599 Freiberg, Germany
2
Institute of Logistics and Aviation, Dresden University of Technology, 01069 Dresden, Germany
*
Author to whom correspondence should be addressed.
The authors contributed equally to this work.
Academic Editor: Joost Ellerbroek
Aerospace 2022, 9(2), 77; https://doi.org/10.3390/aerospace9020077
Received: 5 September 2021 / Revised: 22 January 2022 / Accepted: 26 January 2022 / Published: 1 February 2022
(This article belongs to the Special Issue Application of Data Science to Aviation)
Evaluating the performance of complex systems, such as air traffic management (ATM), is a challenging task. When regarding aviation as a time-continuous system measured in value-discrete time series via performance indicators and certain metrics, it is important to use sufficiently targeted mathematical models within the analysis. A consistent identification of system dynamics at the evaluation level, without dealing with the actual physical events of the system, transforms the analysis of time series into a system identification process, which ensures control of an unknown (or only partially known) system. In this paper, the requirements for mathematical modeling are presented in the form of a step-by-step framework, which can be derived from the formal process model of ATM. The framework is applied to representative datasets based on former experiments and publications, for whose prediction of boarding times and classification of flight delays with machine learning (ML) the framework presented here was used. While the training process of neural networks was described in detail there, the paper shown here focuses on the control options and optimization possibilities based on the trained models. Overall, the discussed framework represents a strict guideline for addressing data and machine learning (ML)-based analysis and metaheuristic optimization in ATM. View Full-Text
Keywords: air traffic management; machine learning; artificial intelligence; data analysis; neural networks; performance benchmarking; optimization air traffic management; machine learning; artificial intelligence; data analysis; neural networks; performance benchmarking; optimization
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MDPI and ACS Style

Reitmann, S.; Schultz, M. An Adaptive Framework for Optimization and Prediction of Air Traffic Management (Sub-)Systems with Machine Learning. Aerospace 2022, 9, 77. https://doi.org/10.3390/aerospace9020077

AMA Style

Reitmann S, Schultz M. An Adaptive Framework for Optimization and Prediction of Air Traffic Management (Sub-)Systems with Machine Learning. Aerospace. 2022; 9(2):77. https://doi.org/10.3390/aerospace9020077

Chicago/Turabian Style

Reitmann, Stefan, and Michael Schultz. 2022. "An Adaptive Framework for Optimization and Prediction of Air Traffic Management (Sub-)Systems with Machine Learning" Aerospace 9, no. 2: 77. https://doi.org/10.3390/aerospace9020077

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