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Classification of air traffic control scenarios using decision trees: insights from a field study in terminal approach radar environment

Malakis, Stathis, Psaros Panagiotis, Kontogiannis Thomas, Malaki Christina

Απλή Εγγραφή


URIhttp://purl.tuc.gr/dl/dias/0ED4464C-04E2-4470-8086-02C0708A4F58-
Αναγνωριστικόhttps://doi.org/10.1007/s10111-019-00562-7-
Αναγνωριστικόhttps://link.springer.com/article/10.1007%2Fs10111-019-00562-7-
Γλώσσαen-
Μέγεθος21 pagesen
ΤίτλοςClassification of air traffic control scenarios using decision trees: insights from a field study in terminal approach radar environmenten
ΔημιουργόςMalakis, Stathisen
ΔημιουργόςPsaros Panagiotisen
ΔημιουργόςKontogiannis Thomasen
ΔημιουργόςΚοντογιαννης Θωμαςel
ΔημιουργόςMalaki Christinaen
ΕκδότηςSpringer Natureen
ΠερίληψηAir traffic controllers are responsible for the safe, expeditious and orderly flow of the air traffic. Their training relies heavily on the use of simulators that can represent various normal and emergency situations. Accurate classification of air traffic scenarios can provide assistance towards a better understanding of how controllers respond to the complexity of a traffic scenario. To this end, we conducted a field study using qualified air traffic controllers, who participated in simulator sessions of terminal radar approach control in a variety of scenarios. The aim of the study was twofold, firstly to explore how decision trees and classification rules can be used for realistic classification of air traffic scenarios and secondly to explore which factors reflect better operational complexity. We applied machine learning methods to the data and developed decision trees and classification rules for these scenarios. Results indicated that decision trees and classification rules are useful tools in accurately categorizing scenarios and that complexity requires a larger set of predictors beyond simple aircraft counts. The derived decision trees and classification rules performed well in prediction, stability and interpretability. Practical benefits can be derived in the areas of operations and system design in the context of air traffic flow and capacity management systems.en
ΤύποςPeer-Reviewed Journal Publicationen
ΤύποςΔημοσίευση σε Περιοδικό με Κριτέςel
Άδεια Χρήσηςhttp://creativecommons.org/licenses/by/4.0/en
Ημερομηνία2021-09-21-
Ημερομηνία Δημοσίευσης2020-
Θεματική ΚατηγορίαAir traffic controlen
Θεματική ΚατηγορίαAir traffic flow managementen
Θεματική ΚατηγορίαMachine learningen
Θεματική ΚατηγορίαDecision treesen
Θεματική ΚατηγορίαClassification rulesen
Θεματική ΚατηγορίαComplexityen
Βιβλιογραφική ΑναφοράS. Malakis, P. Psaros, T. Kontogiannis, and C. Malaki, “Classification of air traffic control scenarios using decision trees: insights from a field study in terminal approach radar environment,” Cogn. Technol. Work, vol. 22, no. 1, pp. 159–179, Feb. 2020. doi: 10.1007/s10111-019-00562-7en

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