Modelling decision-making within rail maintenance control rooms

Dadashi, Nastaran; Golightly, David; Sharples, Sarah

Abstract

This paper presents a cognitive task analysis to derive models of decision-making for rail maintenance processes. Maintenance processes are vital for safe and continuous availability of rail assets and services. These processes are increasingly embracing the ‘Intelligent Infrastructure’ paradigm, which uses automated analysis to predict asset state and potential failure. Understanding the cognitive processes of maintenance operators is critical to underpin design and acceptance of Intelligent Infrastructure. A combination of methods, including observation, interview and an adaptation of critical decision method, was employed to elicit the decision-making strategies of operators in three different types of maintenance control centre, with three configurations of pre-existing technology. The output is a model of decision-making, based on Rasmussen’s decision ladder, that reflects the varying role of automation depending on technology configurations. The analysis also identifies which types of fault were most challenging for operators and identifies the strategies used by operators to manage the concurrent challenges of information deficiencies (both underload and overload). Implications for design are discussed.

Citation

Dadashi, N., Golightly, D., & Sharples, S. (2021). Modelling decision-making within rail maintenance control rooms. Cognition, Technology and Work, 23(2), 255–271. https://doi.org/10.1007/s10111-020-00636-x

• Journal Article Type: Article
• Acceptance Date: Jun 8, 2020
• Online Publication Date: Jun 20, 2020
• Publication Date: 2021-05
• Deposit Date: Jun 22, 2020
• Publicly Available Date: Jun 22, 2020
• Journal: Cognition, Technology & Work
• Print ISSN: 1435-5558
• Electronic ISSN: 1435-5566
• Publisher: Springer Verlag
• Peer Reviewed: Peer Reviewed
• Volume: 23
• Issue: 2
• Pages: 255–271
• DOI: https://doi.org/10.1007/s10111-020-00636-x
• Public URL: https://nottingham-repository.worktribe.com/output/4701968
• Publisher URL: https://link.springer.com/article/10.1007%2Fs10111-020-00636-x
• Additional Information: Received: 31 May 2019; Accepted: 8 June 2020; First Online: 20 June 2020