Determining Air Traffic Controller Proficiency: Identifying Objective Measures Using Clustering

Abstract

Air traffic control (ATC) is a complex and demanding job reserved for highly-trained professionals. Training ATC candidates is challenging as trainees are subjectively assessed by instructors who are biased by their own ways of working. As an effort to determine control expertise objectively, this study employed clustering techniques on an existing data set in which course and professional controllers participated in a medium-fidelity simulation experiment. Results identified a set of eight measures that formed two distinct and stable expertise clusters. A subsequent sensitivity analysis was able to reveal how far (or close) each course participant was positioned from the expert cluster and on which measures those participants deviated from the experts. At this stage, however, it is difficult to translate these results into specific advice on how to improve underdeveloped skills. Despite the small sample size and limited generalizability of the results in this exploratory study, the method appears to be a promising demonstration in determining objective factors that describe ATC expertise, warranting further research.