Identification and Quantification of Contributing Factors to the Criticality of Aircraft Loss of Separation
A Mid-Air Collision (MAC) is a fatal event with tragic consequences. To reduce the risk of a MAC, it is imperative to understand the precursors that trigger it. A primary precursor to a MAC is a loss of separation (LOS) or a separation infringement. This study develops a model to identify the factor...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Aerospace |
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| Online Access: | https://www.mdpi.com/2226-4310/9/9/513 |
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| author | Lidia Serrano-Mira Marta Pérez Maroto Eduardo S. Ayra Javier Alberto Pérez-Castán Schon Z. Y. Liang-Cheng Víctor Gordo Arias Luis Pérez-Sanz |
| author_facet | Lidia Serrano-Mira Marta Pérez Maroto Eduardo S. Ayra Javier Alberto Pérez-Castán Schon Z. Y. Liang-Cheng Víctor Gordo Arias Luis Pérez-Sanz |
| author_sort | Lidia Serrano-Mira |
| collection | DOAJ |
| description | A Mid-Air Collision (MAC) is a fatal event with tragic consequences. To reduce the risk of a MAC, it is imperative to understand the precursors that trigger it. A primary precursor to a MAC is a loss of separation (LOS) or a separation infringement. This study develops a model to identify the factors contributing to a LOS between aircraft pairs. A Bayesian Network (BN) model is used to estimate the conditional dependencies of the factors affecting criticality, that is, how close the LOS has come to becoming a collision. This probabilistic model is built using GeNIe software from data (based on a database created from incident analysis) and expert judgment. The results of the model allow identification of how factors related to the scenario, the human factor (ATC and flight crew) or the technical systems, affect the criticality of the LOS. Based on this information, it is possible to exclude irrelevant elements that do not contribute or whose influence could be neglected, and to prioritize work on the most important ones, in order to increase ATM safety. |
| first_indexed | 2024-03-10T01:04:00Z |
| format | Article |
| id | doaj.art-f45ef5c8e5d24a1d808efa999f54b9bd |
| institution | Directory Open Access Journal |
| issn | 2226-4310 |
| language | English |
| last_indexed | 2024-03-10T01:04:00Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Aerospace |
| spelling | doaj.art-f45ef5c8e5d24a1d808efa999f54b9bd2023-11-23T14:31:11ZengMDPI AGAerospace2226-43102022-09-019951310.3390/aerospace9090513Identification and Quantification of Contributing Factors to the Criticality of Aircraft Loss of SeparationLidia Serrano-Mira0Marta Pérez Maroto1Eduardo S. Ayra2Javier Alberto Pérez-Castán3Schon Z. Y. Liang-Cheng4Víctor Gordo Arias5Luis Pérez-Sanz6ETSI Aeronáutica y del Espacio, Universidad Politécnica de Madrid, Plaza del Cardenal Cisneros, 28040 Madrid, SpainETSI Aeronáutica y del Espacio, Universidad Politécnica de Madrid, Plaza del Cardenal Cisneros, 28040 Madrid, SpainETSI Aeronáutica y del Espacio, Universidad Politécnica de Madrid, Plaza del Cardenal Cisneros, 28040 Madrid, SpainETSI Aeronáutica y del Espacio, Universidad Politécnica de Madrid, Plaza del Cardenal Cisneros, 28040 Madrid, SpainETSI Aeronáutica y del Espacio, Universidad Politécnica de Madrid, Plaza del Cardenal Cisneros, 28040 Madrid, SpainETSI Aeronáutica y del Espacio, Universidad Politécnica de Madrid, Plaza del Cardenal Cisneros, 28040 Madrid, SpainETSI Aeronáutica y del Espacio, Universidad Politécnica de Madrid, Plaza del Cardenal Cisneros, 28040 Madrid, SpainA Mid-Air Collision (MAC) is a fatal event with tragic consequences. To reduce the risk of a MAC, it is imperative to understand the precursors that trigger it. A primary precursor to a MAC is a loss of separation (LOS) or a separation infringement. This study develops a model to identify the factors contributing to a LOS between aircraft pairs. A Bayesian Network (BN) model is used to estimate the conditional dependencies of the factors affecting criticality, that is, how close the LOS has come to becoming a collision. This probabilistic model is built using GeNIe software from data (based on a database created from incident analysis) and expert judgment. The results of the model allow identification of how factors related to the scenario, the human factor (ATC and flight crew) or the technical systems, affect the criticality of the LOS. Based on this information, it is possible to exclude irrelevant elements that do not contribute or whose influence could be neglected, and to prioritize work on the most important ones, in order to increase ATM safety.https://www.mdpi.com/2226-4310/9/9/513air trafficseparation infringementBayesian Network |
| spellingShingle | Lidia Serrano-Mira Marta Pérez Maroto Eduardo S. Ayra Javier Alberto Pérez-Castán Schon Z. Y. Liang-Cheng Víctor Gordo Arias Luis Pérez-Sanz Identification and Quantification of Contributing Factors to the Criticality of Aircraft Loss of Separation Aerospace air traffic separation infringement Bayesian Network |
| title | Identification and Quantification of Contributing Factors to the Criticality of Aircraft Loss of Separation |
| title_full | Identification and Quantification of Contributing Factors to the Criticality of Aircraft Loss of Separation |
| title_fullStr | Identification and Quantification of Contributing Factors to the Criticality of Aircraft Loss of Separation |
| title_full_unstemmed | Identification and Quantification of Contributing Factors to the Criticality of Aircraft Loss of Separation |
| title_short | Identification and Quantification of Contributing Factors to the Criticality of Aircraft Loss of Separation |
| title_sort | identification and quantification of contributing factors to the criticality of aircraft loss of separation |
| topic | air traffic separation infringement Bayesian Network |
| url | https://www.mdpi.com/2226-4310/9/9/513 |
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