A MACHINE LEARNING APPROACH FOR EVALUATING AIR TRAFFIC CONTROLLER CANDIDATES: A COMPARISON WITH AHP AND ANP METHODS

The safety and efficiency of air traffic operations depends heavily on air traffic controllers, making the accurate identification of suitable candidates a critical challenge for air navigation service providers. The selection process is traditionally based on standardized aptitude testing combined with structured interviews and expert evaluation, which may be affected by subjectivity and require substantial time and resources. This study introduces a machine learning based decision support approach for air traffic controller (ATCO) candidate selection. The candidate evaluation task is formulated as a binary classification problem to predict interview outcomes using historical data from an actual selection process involving 194 candidates, including 30 who passed the interview phase. Three machine learning models, Logistic Regression (LR), Support Vector Machine (SVM), and Decision Tree (DT), are trained using candidate background variables such as prior examination scores, type of high school attended, and high school grade point average. The performance of the models is evaluated in comparison with existing approaches based on the Analytic Hierarchy Process and the Analytic Network Process. The results of this study provide preliminary evidence that, in the context of ATCO selection, machine learning models may approximate the outcomes of traditional interviews, with LR achieving an accuracy of 93%, and both DT and SVM achieving accuracies of 90%. Using the best-performing model, 46 eliminated candidates were correctly classified as eliminated (true negatives) and 9 selected candidates were correctly identified as selected (true positives), while 4 eliminated candidates were incorrectly classified as selected (false positives). These results suggest that the proposed approach may support air navigation service providers and related institutions by assisting early-stage screening through data driven prioritization of ATCO candidates. © (2026), (Creative Decisions Foundation). All right reserved.