Print Email Facebook Twitter Determining Air Traffic Controller Proficiency Title Determining Air Traffic Controller Proficiency: Identifying a Set of Measures Using Machine Learning Author de Jong, Tjitte (TU Delft Aerospace Engineering; TU Delft Control & Operations) Contributor Borst, C. (mentor) Eisma, Y.B. (graduation committee) van Paassen, M.M. (graduation committee) Mulder, Max (graduation committee) Degree granting institution Delft University of Technology Programme Aerospace Engineering Date 2019-01-29 Abstract A high drop-out rate is present during current-day air traffic controller (ATCo) training, because the required expertise level is not reached. The determination of the expertise level of ATCo students is currently performed using subjective assessments at a late stage in the training by means of high-fidelity simulator sessions. It is desired to objectively measure expertise earlier and more frequently in training to monitor the progress of the student. However, it is currently unknown which objective measures might describe the expertise level of an ATCo. This paper presents a method that identifies a set of objective measures that can classify an ATCo's expertise level using a genetic algorithm and hierarchical agglomerative clustering. A large set of possible objective measures and a dataset containing data from 10 ATCos (intermediate and pro level) is used. The method found a set of 8 measures that can cluster the 10 ATCo's in the two expertise groups very accurately (97,5% accuracy). The genetic algorithm showed a preference for measures that have a distinction in the results between the expertise groups. However, not all selected measures show a difference between the expertise groups, resulting in signs of overfitting. Furthermore, these measures only provided limited feedback for the individual ATCos. Clustering the results of the 10 ATCo's gave valuable information about the overall expertise level of an ATCo, as compared to the average intermediate- or pro-ATCo. Subject Air traffic controlproficiencyexpertise levelmachine learninggenetic algorithmhierarchical agglomerative clustering To reference this document use: http://resolver.tudelft.nl/uuid:541f7ea4-ef42-4447-97b7-ad1b7e2a681f Embargo date 2024-01-29 Part of collection Student theses Document type master thesis Rights © 2019 Tjitte de Jong Files PDF Final_Thesis_Report_TP_de ... ciency.pdf 4.8 MB Close viewer /islandora/object/uuid:541f7ea4-ef42-4447-97b7-ad1b7e2a681f/datastream/OBJ/view