Air traffc controller workload is an important impediment to air transport growth. Several approaches exist that aim to better understand the causes for workload, and models have been derived to predict workload in new operational settings. These methods often relate workload to the diffculty, or complexity, that an average controller would have to safely manage all traffc in a sector with a particular traffc demand. In this paper, several of these complexity-based metrics for workload will be compared. Of special interest is whether the complexity measures transfer from one sector design to another. That is, does a metric that is well-tuned to predict workload for controllers working in one sector, also predict the workload for another group of controllers active in a different sector? Results from a human-in-the-loop experiment show that a solution space-based metric, which requires no tuning or weighing at all, has the highest correlations with subjectively reported workload, and also yields the best workload predictions across different controller groups and sectors.
|Publication status||Published - 2016|
|Event||13th IFAC Symposium on Analysis, Design, and Evaluation of Human-Machine Systems - Kyoto, Japan|
Duration: 30 Aug 2016 → 2 Sep 2016
- Air traffc control
- mental workload
- supervisory control