Please use this identifier to cite or link to this item:
https://doi.org/10.21256/zhaw-26455| 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 10.21256/zhaw-26455 |
| 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. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/26455 |
| Related research data: | https://doi.org/10.5281/zenodo.7148117 |
| 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 |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 2022_Monstein-etal_Large-landing-trajectory-dataset-Go-Around-analysis.pdf | 986.09 kB | Adobe PDF |  View/Open |
Show full item record
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. https://doi.org/10.3390/engproc2022028002
Monstein, R. et al. (2022) ‘Large landing trajectory dataset for go-around analysis’, in Engineering Proceedings. MDPI, p. 2. Available at: https://doi.org/10.3390/engproc2022028002.
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. https://doi.org/10.3390/engproc2022028002.
Monstein, Raphael, et al. “Large Landing Trajectory Dataset for Go-around Analysis.” Engineering Proceedings, vol. 28, no. 1, MDPI, 2022, p. 2, https://doi.org/10.3390/engproc2022028002.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.