Airspace Operations Lab @ NASA Ames

Controller workload, recognized as a significant bottleneck to capacity increase in the future National Airspace System, has been researched extensively in air traffic management. Unfortunately, subjective workload has been an unreliable predictor of a controller�s ability to safely manage the traffic, leading to attempts at replacing workload with more objective metrics, such as task load (e.g. number of clearances) and traffic density (e.g. aircraft count). A significant caveat in substituting these metrics for workload ratings, however, is that their relationships are non- linear. More specifically, as traffic increases linearly, controller�s perceived workload remains relatively constant until the traffic and associated task load reach a critical threshold. From this point, the workload increases at a much faster rate with each added aircraft. In an informal "traffic load test", researchers at NASA Ames Research Center manipulated the aircraft count in real-time human-in-the-loop simulations to determine the maximum traffic level at which the controllers reported the traffic to be no longer manageable. As hypothesized, traffic scenarios that were rated as moderate workload quickly became unmanageable with few additional aircraft. Feedback from the controllers further supported the non-linear nature of subjective workload. Task load data partially supported the above findings but the results were inconclusive due to differences in findings between various task load metrics. The non-linear relationship between subjective workload and aircraft count has been further examined using data from Free Maneuvering concept feasibility study in June 2004. The results showed a step-function relationship between workload and aircraft count, suggesting that controllers perceive workload as categorical. The combined results suggest that any estimation on workload should not be extrapolated linearly from a set of workload measures taken from an experiment since the extrapolated workload is likely to significantly underestimate workload.