Predicting airplane go-arounds using machine learning and open-source data

Figuet, Benoit; Monstein, Raphael; Waltert, Manuel; Barry, Steven

doi:10.3390/proceedings2020059006

Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-21209

Publication type:	Conference paper
Type of review:	Peer review (publication)
Title:	Predicting airplane go-arounds using machine learning and open-source data
Authors:	Figuet, Benoit Monstein, Raphael Waltert, Manuel Barry, Steven
et. al:	No
DOI:	10.3390/proceedings2020059006 10.21256/zhaw-21209
Published in:	Proceedings
Volume(Issue):	59
Issue:	6
Conference details:	8th OpenSky Symposium, online, 12-13 November 2020
Issue Date:	1-Dec-2020
Publisher / Ed. Institution:	MDPI
ISSN:	2504-3900
Language:	English
Subjects:	OpenSky network; Go-around; Prediction; Machine learning; Generalized additive model; ADS-B
Subject (DDC):	006: Special computer methods 380: Transportation
Abstract:	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.
URI:	https://digitalcollection.zhaw.ch/handle/11475/21209
Fulltext version:	Published version
License (according to publishing contract):	CC BY 4.0: Attribution 4.0 International
Departement:	School of Engineering
Organisational Unit:	Centre for Aviation (ZAV)
Appears in collections:	Publikationen School of Engineering

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Figuet, B., Monstein, R., Waltert, M., & Barry, S. (2020). Predicting airplane go-arounds using machine learning and open-source data [Conference paper]. Proceedings, 59(6). https://doi.org/10.3390/proceedings2020059006

Figuet, B. et al. (2020) ‘Predicting airplane go-arounds using machine learning and open-source data’, in Proceedings. MDPI. Available at: https://doi.org/10.3390/proceedings2020059006.

B. Figuet, R. Monstein, M. Waltert, and S. Barry, “Predicting airplane go-arounds using machine learning and open-source data,” in Proceedings, Dec. 2020, vol. 59, no. 6. doi: 10.3390/proceedings2020059006.

FIGUET, Benoit, Raphael MONSTEIN, Manuel WALTERT und Steven BARRY, 2020. Predicting airplane go-arounds using machine learning and open-source data. In: Proceedings. Conference paper. MDPI. 1 Dezember 2020

Figuet, Benoit, Raphael Monstein, Manuel Waltert, and Steven Barry. 2020. “Predicting Airplane Go-Arounds Using Machine Learning and Open-Source Data.” Conference paper. In Proceedings. Vol. 59. MDPI. https://doi.org/10.3390/proceedings2020059006.

Figuet, Benoit, et al. “Predicting Airplane Go-Arounds Using Machine Learning and Open-Source Data.” Proceedings, vol. 59, no. 6, MDPI, 2020, https://doi.org/10.3390/proceedings2020059006.