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Evaluating the Operational Impact of Tactics Enabled by an AI-Based Decision Support

Paper ID

SIDs-2023-41

Conference

SESAR Innovation Days

Year

2023

Theme

Human Factors and Decision Support Tools

Project Name

SESAR 2020 ER4 project SafeOPS

Keywords:

air traffic control, decision support, Go-around, Machine learning, Operational Evaluation, Resilience, safety

Authors

Lukas Beller, Gökay Özer, Marco Pfahler, Phillip Koppitz and Florian Holzapfel

DOI

https://doi.org/10.61009/SID.2023.1.25

Project Number

892919

Abstract

Machine learning-based go-around predictions have been discussed in the research community for some years. Much work has been done developing algorithms and testing their accuracy, motivated by the assumption that time-in-advance information on the go-around likelihood of arrival aircraft will benefit air traffic controllers. The question of how to incorporate predictive and probabilistic information into the operation and how to evaluate their operational impact has yet to be investigated. This paper presents a first step toward assessing the operational impact of a machine learning-based decision support tool. Therefore, a low-fidelity, human-in-the-loop simulation exercise with air traffic controllers discovers potential new tactics enabled by a go-around prediction tool and evaluates them regarding safety, resilience, and capacity.