Print Email Facebook Twitter Cross-sector transferability of metrics for air traffic controller workload Title Cross-sector transferability of metrics for air traffic controller workload Author Abdul Rahman, S.M.B. Borst, C. (TU Delft Control & Simulation) van Paassen, M.M. (TU Delft Control & Simulation) Mulder, Max (TU Delft Control & Operations) Department Control & Operations Date 2016 Abstract 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. Subject Air traffc controltaskloadmental workloadsupervisory control To reference this document use: http://resolver.tudelft.nl/uuid:ad652b50-4e6c-44bb-8ed6-f7bd3ab7d517 DOI https://doi.org/10.1016/j.ifacol.2016.10.561 ISSN 2405-8963 Source IFAC-PapersOnLine, 49 (19), 313-318 Event 13th IFAC Symposium on Analysis, Design, and Evaluation of Human-Machine Systems, 2016-08-30 → 2016-09-02, Kyoto, Japan Part of collection Institutional Repository Document type journal article Rights © 2016 S.M.B. Abdul Rahman, C. Borst, M.M. van Paassen, Max Mulder Files PDF IFAC2016_MariamRahman.pdf 1.24 MB Close viewer /islandora/object/uuid:ad652b50-4e6c-44bb-8ed6-f7bd3ab7d517/datastream/OBJ/view