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Impact of automation: measurement of performance, workload and behaviour in a complex control environment

Balfe, Nora; Sharples, Sarah; Wilson, John R.

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Authors

Nora Balfe

SARAH SHARPLES SARAH.SHARPLES@NOTTINGHAM.AC.UK
Professor of Human Factors

John R. Wilson



Abstract

This paper describes an experiment that was undertaken to compare three levels of automation in rail signalling; a high level in which an automated agent set routes for trains using timetable information, a medium level in which trains were routed along pre-defined paths, and a low level where the operator (signaller) was responsible for the movement of all trains. These levels are described in terms of a Rail Automation Model based on previous automation theory (Parasuraman et al., 2000). Performance, subjective workload, and signaller activity were measured for each level of automation running under both normal operating conditions and abnormal, or disrupted, conditions. The results indicate that perceived workload, during both normal and disrupted phases of the experiment, decreased as the level of automation increased and performance was most consistent (i.e. showed the least variation between participants) with the highest level of automation. The results give a strong case in favour of automation, particularly in terms of demonstrating the potential for automation to reduce workload, but also suggest much benefit can achieved from a mid-level of automation potentially at a lower cost and complexity.

Citation

Balfe, N., Sharples, S., & Wilson, J. R. (2014). Impact of automation: measurement of performance, workload and behaviour in a complex control environment. Applied Ergonomics, 47, https://doi.org/10.1016/j.apergo.2014.08.002

Journal Article Type Article
Publication Date Sep 19, 2014
Deposit Date Nov 9, 2015
Publicly Available Date Nov 9, 2015
Journal Applied Ergonomics
Print ISSN 0003-6870
Electronic ISSN 0003-6870
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 47
DOI https://doi.org/10.1016/j.apergo.2014.08.002
Keywords Automation, Rail human factors, Workload, Performance
Public URL https://nottingham-repository.worktribe.com/output/735630
Publisher URL http://www.sciencedirect.com/science/article/pii/S0003687014001410

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