Determining Flight Complexity and Relevance: Flight-Centric Filtering for Air Traffic Control

Air Traffic Controllers (ATCOs) ensure safe and efficient operations by scanning radar displays to identify flights needing clearances. They then compare flight parameters to assess the impact of potential actions on sector safety. With global air traffic expected to rise, comparing flight labels will become more time-consuming, increasing workload and response delays. To ease this cognitive burden, a flight filtering mechanism is introduced, focusing on flights with spatio-temporal proximities to a selected flight of interest. Based on data from a previous study involving five professional controllers, filter parameters and their thresholds have been selected and tuned. Results indicate that filtering by consolidated state- and intent-based interaction parameters yield the best match to controllers’ judgements about relevant flights relative to a flight of interest. It is anticipated that the filter, outputting a list of relevant flights, can serve as an operational support tool by fading non-relevant flights, reducing cognitive effort in visual searches, and could aid Flight-Centric Air Traffic Control (ATC) allocation models that are based on predicting flight-centric complexity.