Predicting Airplane Go-Arounds Using Machine Learning and Open-Source Data
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 d...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-12-01
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| Series: | Proceedings |
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| Online Access: | https://www.mdpi.com/2504-3900/59/1/6 |
| _version_ | 1797546138375553024 |
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| author | Benoit Figuet Raphael Monstein Manuel Waltert Steven Barry |
| author_facet | Benoit Figuet Raphael Monstein Manuel Waltert Steven Barry |
| author_sort | Benoit Figuet |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T14:25:50Z |
| format | Article |
| id | doaj.art-fee12c95460943dca6cf799a84601985 |
| institution | Directory Open Access Journal |
| issn | 2504-3900 |
| language | English |
| last_indexed | 2024-03-10T14:25:50Z |
| publishDate | 2020-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Proceedings |
| spelling | doaj.art-fee12c95460943dca6cf799a846019852023-11-20T23:04:38ZengMDPI AGProceedings2504-39002020-12-01591610.3390/proceedings2020059006Predicting Airplane Go-Arounds Using Machine Learning and Open-Source DataBenoit Figuet0Raphael Monstein1Manuel Waltert2Steven Barry3Centre for Aviation, School of Engineering, Zurich University of Applied Sciences, 8401 Winterthur, SwitzerlandCentre for Aviation, School of Engineering, Zurich University of Applied Sciences, 8401 Winterthur, SwitzerlandCentre for Aviation, School of Engineering, Zurich University of Applied Sciences, 8401 Winterthur, SwitzerlandSafety and Assurance, Airservices Australia, Canberra, ACT 2601, AustraliaGo-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.https://www.mdpi.com/2504-3900/59/1/6OpenSky networkgo-aroundpredictionmachine learninggeneralized additive modelADS-B |
| spellingShingle | Benoit Figuet Raphael Monstein Manuel Waltert Steven Barry Predicting Airplane Go-Arounds Using Machine Learning and Open-Source Data Proceedings OpenSky network go-around prediction machine learning generalized additive model ADS-B |
| title | Predicting Airplane Go-Arounds Using Machine Learning and Open-Source Data |
| title_full | Predicting Airplane Go-Arounds Using Machine Learning and Open-Source Data |
| title_fullStr | Predicting Airplane Go-Arounds Using Machine Learning and Open-Source Data |
| title_full_unstemmed | Predicting Airplane Go-Arounds Using Machine Learning and Open-Source Data |
| title_short | Predicting Airplane Go-Arounds Using Machine Learning and Open-Source Data |
| title_sort | predicting airplane go arounds using machine learning and open source data |
| topic | OpenSky network go-around prediction machine learning generalized additive model ADS-B |
| url | https://www.mdpi.com/2504-3900/59/1/6 |
| work_keys_str_mv | AT benoitfiguet predictingairplanegoaroundsusingmachinelearningandopensourcedata AT raphaelmonstein predictingairplanegoaroundsusingmachinelearningandopensourcedata AT manuelwaltert predictingairplanegoaroundsusingmachinelearningandopensourcedata AT stevenbarry predictingairplanegoaroundsusingmachinelearningandopensourcedata |