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Publication type: Conference paper
Type of review: Peer review (abstract)
Title: Large landing trajectory dataset for go-around analysis
Authors: Monstein, Raphael
Figuet, Benoit
Krauth, Timothé
Waltert, Manuel
Dettling, Marcel
et. al: No
DOI: 10.3390/engproc2022028002
Published in: Engineering Proceedings
Volume(Issue): 28
Issue: 1
Page(s): 2
Conference details: 10th OpenSky Symposium, Delft, The Netherlands, 10-11 November 2022
Issue Date: 13-Dec-2022
Publisher / Ed. Institution: MDPI
ISSN: 2673-4591
Language: English
Subjects: ADS-B; Go-around; Missed approach; OpenSky network; Dataset
Subject (DDC): 005: Computer programming, programs and data
629: Aeronautical, automotive engineering
Abstract: The analysis and prediction of go-arounds, also referred to as missed approaches, is an active field of research due to the go-around’s impact on safety and the disruption of the traffic flow at airports. The advent of open-source aircraft trajectories available to researchers has increased the level of interest in the field. This paper introduces a publicly available dataset containing metadata of almost 9 million landings and 33,000 go-arounds. The dataset is based on observations from the OpenSky Network and includes data from 176 airports in 44 countries observed in the year 2019. After downloading the data, a go-around classification was performed and the quality was assessed. The usefulness of the dataset is illustrated with two novel example applications. The first example shows how the go-around rate for a runway can be modeled by using a quasi-binomial generalized linear model, while the second example compares the go-around rates for a number of airport–airline pairs. The introduced dataset is significantly larger than the data used so far in the analysis of go-arounds and provides the opportunity to develop novel use cases. This dataset frees researchers from having to collect and process large amounts of data and instead lets them focus on the analysis. The authors are convinced that this large dataset will stoke the creativity of the research community and facilitate interesting and novel applications.
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Fulltext version: Published version
License (according to publishing contract): CC BY 4.0: Attribution 4.0 International
Departement: School of Engineering
Organisational Unit: Institute of Data Analysis and Process Design (IDP)
Centre for Aviation (ZAV)
Appears in collections:Publikationen School of Engineering

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Monstein, R., Figuet, B., Krauth, T., Waltert, M., & Dettling, M. (2022). Large landing trajectory dataset for go-around analysis [Conference paper]. Engineering Proceedings, 28(1), 2.
Monstein, R. et al. (2022) ‘Large landing trajectory dataset for go-around analysis’, in Engineering Proceedings. MDPI, p. 2. Available at:
R. Monstein, B. Figuet, T. Krauth, M. Waltert, and M. Dettling, “Large landing trajectory dataset for go-around analysis,” in Engineering Proceedings, Dec. 2022, vol. 28, no. 1, p. 2. doi: 10.3390/engproc2022028002.
MONSTEIN, Raphael, Benoit FIGUET, Timothé KRAUTH, Manuel WALTERT und Marcel DETTLING, 2022. Large landing trajectory dataset for go-around analysis. In: Engineering Proceedings. Conference paper. MDPI. 13 Dezember 2022. S. 2
Monstein, Raphael, Benoit Figuet, Timothé Krauth, Manuel Waltert, and Marcel Dettling. 2022. “Large Landing Trajectory Dataset for Go-around Analysis.” Conference paper. In Engineering Proceedings, 28:2. MDPI.
Monstein, Raphael, et al. “Large Landing Trajectory Dataset for Go-around Analysis.” Engineering Proceedings, vol. 28, no. 1, MDPI, 2022, p. 2,

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