Study of the Impact of Traffic Flows on the ATC Actions
It has always been a topic of great interest in air transport management to be able to estimate controller workload. So far, research has not had the opportunity to make use of real data on the controller’s actions. We have enough data to be able to use machine learning methods. The aim of this work...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Aerospace |
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| Online Access: | https://www.mdpi.com/2226-4310/9/8/467 |
| _version_ | 1797440198713278464 |
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| author | Guillermo Gutiérrez Teuler Rosa María Arnaldo Valdés Victor Fernando Gómez Comendador Patricia María López de Frutos Rubén Rodríguez Rodríguez |
| author_facet | Guillermo Gutiérrez Teuler Rosa María Arnaldo Valdés Victor Fernando Gómez Comendador Patricia María López de Frutos Rubén Rodríguez Rodríguez |
| author_sort | Guillermo Gutiérrez Teuler |
| collection | DOAJ |
| description | It has always been a topic of great interest in air transport management to be able to estimate controller workload. So far, research has not had the opportunity to make use of real data on the controller’s actions. We have enough data to be able to use machine learning methods. The aim of this work is to predict the controller’s actions to know his workload. Several machine learning models were tested to try different combinations of features and the selected algorithms and two models were finally chosen. The predictions provided by the models are good enough to be used when a first approximation of the workload in a sector is to be obtained. Finally, explainability techniques were employed to discover the patterns found by the AI in the machine learning models. Thanks to these techniques, we can build a profile of the critical flights that increase the workload the most. |
| first_indexed | 2024-03-09T12:03:41Z |
| format | Article |
| id | doaj.art-65e84fd555ae47df9a63e6bd897d4da1 |
| institution | Directory Open Access Journal |
| issn | 2226-4310 |
| language | English |
| last_indexed | 2024-03-09T12:03:41Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Aerospace |
| spelling | doaj.art-65e84fd555ae47df9a63e6bd897d4da12023-11-30T23:00:12ZengMDPI AGAerospace2226-43102022-08-019846710.3390/aerospace9080467Study of the Impact of Traffic Flows on the ATC ActionsGuillermo Gutiérrez Teuler0Rosa María Arnaldo Valdés1Victor Fernando Gómez Comendador2Patricia María López de Frutos3Rubén Rodríguez Rodríguez4Aerospace Systems, Air Transport and Airports Department (SATAA), School of Aeronautical and Space Engineering (ETSIAE), Polytechnic University of Madrid (UPM), 28040 Madrid, SpainAerospace Systems, Air Transport and Airports Department (SATAA), School of Aeronautical and Space Engineering (ETSIAE), Polytechnic University of Madrid (UPM), 28040 Madrid, SpainAerospace Systems, Air Transport and Airports Department (SATAA), School of Aeronautical and Space Engineering (ETSIAE), Polytechnic University of Madrid (UPM), 28040 Madrid, SpainReference Centre for Research, Development and ATM Innovation (CRIDA), 28040 Madrid, SpainReference Centre for Research, Development and ATM Innovation (CRIDA), 28040 Madrid, SpainIt has always been a topic of great interest in air transport management to be able to estimate controller workload. So far, research has not had the opportunity to make use of real data on the controller’s actions. We have enough data to be able to use machine learning methods. The aim of this work is to predict the controller’s actions to know his workload. Several machine learning models were tested to try different combinations of features and the selected algorithms and two models were finally chosen. The predictions provided by the models are good enough to be used when a first approximation of the workload in a sector is to be obtained. Finally, explainability techniques were employed to discover the patterns found by the AI in the machine learning models. Thanks to these techniques, we can build a profile of the critical flights that increase the workload the most.https://www.mdpi.com/2226-4310/9/8/467air traffic managementartificial intelligenceair traffic controlmental workloadmachine learningexplainable AI |
| spellingShingle | Guillermo Gutiérrez Teuler Rosa María Arnaldo Valdés Victor Fernando Gómez Comendador Patricia María López de Frutos Rubén Rodríguez Rodríguez Study of the Impact of Traffic Flows on the ATC Actions Aerospace air traffic management artificial intelligence air traffic control mental workload machine learning explainable AI |
| title | Study of the Impact of Traffic Flows on the ATC Actions |
| title_full | Study of the Impact of Traffic Flows on the ATC Actions |
| title_fullStr | Study of the Impact of Traffic Flows on the ATC Actions |
| title_full_unstemmed | Study of the Impact of Traffic Flows on the ATC Actions |
| title_short | Study of the Impact of Traffic Flows on the ATC Actions |
| title_sort | study of the impact of traffic flows on the atc actions |
| topic | air traffic management artificial intelligence air traffic control mental workload machine learning explainable AI |
| url | https://www.mdpi.com/2226-4310/9/8/467 |
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