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...
Published in: | IFAC-PapersOnLine |
---|---|
Main Author: | |
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 |
_version_ |
1820775475243384832 |