Unearthing air traffic control officer strategies from simulated air traffic data

With the growth in air traffic volume, automation tools are being developed to increase the capabilities of Air Traffic Control Officers (ATCOs). In this paper, a novel approach to unearth Air Traffic Control (ATC) strategies from raw simulator data is described by utilizing executed radar commands obtained via mouse click data. Five sets of air traffic simulation exercise data were used to identify potential conflicts and unearth likely strategies undertaken using a proposed strategy identification model. The preliminary results demonstrate the success of the model in its ability to identify four distinct strategies adopted by the controllers to safely navigate air traffic conflicts that occurred during the simulation and the conflict type in which they occurred. Strategies identified were also verified by an expert panel to be effective in solving the targeted conflict type. The proposed model can be used to objectively identify ATC strategies for use in automation development.