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Proceeding Paper

Predicting Airplane Go-Arounds Using Machine Learning and Open-Source Data †

1
Centre for Aviation, School of Engineering, Zurich University of Applied Sciences, 8401 Winterthur, Switzerland
2
Safety and Assurance, Airservices Australia, Canberra, ACT 2601, Australia
*
Author to whom correspondence should be addressed.
Presented at the 8th OpenSky Symposium 2020, Online, 12–13 November 2020.
Proceedings 2020, 59(1), 6; https://doi.org/10.3390/proceedings2020059006
Published: 1 December 2020
(This article belongs to the Proceedings of 8th OpenSky Symposium 2020)
Go-arounds (GAs) are standard air traffic control procedures during which aircraft approach a runway but do not land. The incidence of a GA can subsequently affect the workload of flight crews and air traffic controllers, and might impact an airport runway’s throughput capacity. In this study, two different modeling methods for predicting the occurrence of GAs based on open-source Automatic Dependent Surveillance–Broadcast (ADS-B) and meteorological data are presented. A macroscopic model quantifies the probability of a GA within the next hour for an airport by applying a generalized additive model. A microscopic model employs a number of machine learning classifiers on trajectories of aircraft on approach in order to predict if a GA will be performed. Even though the results of the macroscopic model are promising, the information currently available to predict the probability of a GA is not detailed enough to achieve satisfactory predictions. Similarly, the microscopic model is capable of predicting 50% of all GAs, with false positive rate below 7%. Despite the limitations of the quality of the results, the authors are convinced that both modeling methods can be inspiring to other researchers and provide useful insights into the airport system under scrutiny.
Keywords: OpenSky network; go-around; prediction; machine learning; generalized additive model; ADS-B OpenSky network; go-around; prediction; machine learning; generalized additive model; ADS-B
MDPI and ACS Style

Figuet, B.; Monstein, R.; Waltert, M.; Barry, S. Predicting Airplane Go-Arounds Using Machine Learning and Open-Source Data. Proceedings 2020, 59, 6. https://doi.org/10.3390/proceedings2020059006

AMA Style

Figuet B, Monstein R, Waltert M, Barry S. Predicting Airplane Go-Arounds Using Machine Learning and Open-Source Data. Proceedings. 2020; 59(1):6. https://doi.org/10.3390/proceedings2020059006

Chicago/Turabian Style

Figuet, Benoit, Raphael Monstein, Manuel Waltert, and Steven Barry. 2020. "Predicting Airplane Go-Arounds Using Machine Learning and Open-Source Data" Proceedings 59, no. 1: 6. https://doi.org/10.3390/proceedings2020059006

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