Data-Driven Modeling of Air Traffic Controllers’ Policy to Resolve Conflicts
With the aim to enhance automation in conflict detection and resolution (CD&R) tasks in the air traffic management (ATM) domain, this article studies the use of artificial intelligence and machine learning (AI/ML) methods to learn air traffic controllers’ (ATCOs) policy in resolving conflicts am...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-06-01
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| Series: | Aerospace |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2226-4310/10/6/557 |
| _version_ | 1797596616826290176 |
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| author | Alevizos Bastas George A. Vouros |
| author_facet | Alevizos Bastas George A. Vouros |
| author_sort | Alevizos Bastas |
| collection | DOAJ |
| description | With the aim to enhance automation in conflict detection and resolution (CD&R) tasks in the air traffic management (ATM) domain, this article studies the use of artificial intelligence and machine learning (AI/ML) methods to learn air traffic controllers’ (ATCOs) policy in resolving conflicts among aircraft assessed to violate separation minimum constraints during the en route phase of flights, in the tactical phase of operations. The objective is to model <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>h</mi><mi>o</mi><mi>w</mi></mrow></semantics></math></inline-formula> conflicts are being resolved by ATCOs. Towards this goal, the article formulates the ATCO policy learning problem for conflict resolution, addresses the challenging issue of an inherent lack of information in real-world data, and presents AI/ML methods that learn models of ATCOs’ behavior. The methods are evaluated using real-world datasets. The results show that AI/ML methods can achieve good accuracy on predicting ATCOs’ actions given specific conflicts, revealing the preferences of ATCOs for resolution actions in specific circumstances. However, the high accuracy of predictions is hindered by real-world data-inherent limitations. |
| first_indexed | 2024-03-11T02:52:41Z |
| format | Article |
| id | doaj.art-49387d7accff480fbb8d1fe9c9029433 |
| institution | Directory Open Access Journal |
| issn | 2226-4310 |
| language | English |
| last_indexed | 2024-03-11T02:52:41Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Aerospace |
| spelling | doaj.art-49387d7accff480fbb8d1fe9c90294332023-11-18T08:50:16ZengMDPI AGAerospace2226-43102023-06-0110655710.3390/aerospace10060557Data-Driven Modeling of Air Traffic Controllers’ Policy to Resolve ConflictsAlevizos Bastas0George A. Vouros1University of Piraeus Research Center, Department of Digital Systems, University of Piraeus, 18534 Piraeus, GreeceUniversity of Piraeus Research Center, Department of Digital Systems, University of Piraeus, 18534 Piraeus, GreeceWith the aim to enhance automation in conflict detection and resolution (CD&R) tasks in the air traffic management (ATM) domain, this article studies the use of artificial intelligence and machine learning (AI/ML) methods to learn air traffic controllers’ (ATCOs) policy in resolving conflicts among aircraft assessed to violate separation minimum constraints during the en route phase of flights, in the tactical phase of operations. The objective is to model <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>h</mi><mi>o</mi><mi>w</mi></mrow></semantics></math></inline-formula> conflicts are being resolved by ATCOs. Towards this goal, the article formulates the ATCO policy learning problem for conflict resolution, addresses the challenging issue of an inherent lack of information in real-world data, and presents AI/ML methods that learn models of ATCOs’ behavior. The methods are evaluated using real-world datasets. The results show that AI/ML methods can achieve good accuracy on predicting ATCOs’ actions given specific conflicts, revealing the preferences of ATCOs for resolution actions in specific circumstances. However, the high accuracy of predictions is hindered by real-world data-inherent limitations.https://www.mdpi.com/2226-4310/10/6/557air traffic managementconflict detection and resolutionmachine learning |
| spellingShingle | Alevizos Bastas George A. Vouros Data-Driven Modeling of Air Traffic Controllers’ Policy to Resolve Conflicts Aerospace air traffic management conflict detection and resolution machine learning |
| title | Data-Driven Modeling of Air Traffic Controllers’ Policy to Resolve Conflicts |
| title_full | Data-Driven Modeling of Air Traffic Controllers’ Policy to Resolve Conflicts |
| title_fullStr | Data-Driven Modeling of Air Traffic Controllers’ Policy to Resolve Conflicts |
| title_full_unstemmed | Data-Driven Modeling of Air Traffic Controllers’ Policy to Resolve Conflicts |
| title_short | Data-Driven Modeling of Air Traffic Controllers’ Policy to Resolve Conflicts |
| title_sort | data driven modeling of air traffic controllers policy to resolve conflicts |
| topic | air traffic management conflict detection and resolution machine learning |
| url | https://www.mdpi.com/2226-4310/10/6/557 |
| work_keys_str_mv | AT alevizosbastas datadrivenmodelingofairtrafficcontrollerspolicytoresolveconflicts AT georgeavouros datadrivenmodelingofairtrafficcontrollerspolicytoresolveconflicts |