Cross-sector transferability of metrics for air traffic controller workload

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 co...

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Published in:IFAC-PapersOnLine
Main Author: Rahman S.M.B.A.; Borst C.; Paassen M.M.V.; Mulder M.
Format: Conference paper
Language:English
Published: Elsevier B.V. 2016
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-84994807291&doi=10.1016%2fj.ifacol.2016.10.561&partnerID=40&md5=e2477da3703188b23566b47f824e01b2
id 2-s2.0-84994807291
spelling 2-s2.0-84994807291
Rahman S.M.B.A.; Borst C.; Paassen M.M.V.; Mulder M.
Cross-sector transferability of metrics for air traffic controller workload
2016
IFAC-PapersOnLine
49
19
10.1016/j.ifacol.2016.10.561
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84994807291&doi=10.1016%2fj.ifacol.2016.10.561&partnerID=40&md5=e2477da3703188b23566b47f824e01b2
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. © 2016
Elsevier B.V.
24058963
English
Conference paper
All Open Access; Gold Open Access; Green Open Access
author Rahman S.M.B.A.; Borst C.; Paassen M.M.V.; Mulder M.
spellingShingle Rahman S.M.B.A.; Borst C.; Paassen M.M.V.; Mulder M.
Cross-sector transferability of metrics for air traffic controller workload
author_facet Rahman S.M.B.A.; Borst C.; Paassen M.M.V.; Mulder M.
author_sort Rahman S.M.B.A.; Borst C.; Paassen M.M.V.; Mulder M.
title Cross-sector transferability of metrics for air traffic controller workload
title_short Cross-sector transferability of metrics for air traffic controller workload
title_full Cross-sector transferability of metrics for air traffic controller workload
title_fullStr Cross-sector transferability of metrics for air traffic controller workload
title_full_unstemmed Cross-sector transferability of metrics for air traffic controller workload
title_sort Cross-sector transferability of metrics for air traffic controller workload
publishDate 2016
container_title IFAC-PapersOnLine
container_volume 49
container_issue 19
doi_str_mv 10.1016/j.ifacol.2016.10.561
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-84994807291&doi=10.1016%2fj.ifacol.2016.10.561&partnerID=40&md5=e2477da3703188b23566b47f824e01b2
description 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. © 2016
publisher Elsevier B.V.
issn 24058963
language English
format Conference paper
accesstype All Open Access; Gold Open Access; Green Open Access
record_format scopus
collection Scopus
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