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Big data and ergonomics methods: A new paradigm for tackling strategic transport safety risks

Walker, Guy and Strathie, Ailsa (2016). Big data and ergonomics methods: A new paradigm for tackling strategic transport safety risks. Applied Ergonomics, 53(Part B) pp. 298–311.

DOI (Digital Object Identifier) Link: https://doi.org/10.1016/j.apergo.2015.09.008
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Abstract

Big data collected from On-Train Data Recorders (OTDR) has the potential to address the most important strategic risks currently faced by rail operators and authorities worldwide. These risk issues are increasingly orientated around human performance and have proven resistant to existing approaches. This paper presents a number of proof of concept demonstrations to show that long standing ergonomics methods can be driven from big data, and succeed in providing insight into human performance in a novel way. Over 300 ergonomics methods were reviewed and a smaller sub-set selected for proof-of-concept development using real on-train recorder data. From this are derived nine candidate Human Factors Leading Indicators which map on to all of the psychological precursors of the identified risks. This approach has the potential to make use of a significantly underused source of data, and enable rail industry stakeholders to intervene sooner to address human performance issues that, via the methods presented in this paper, are clearly manifest in on-train data recordings. The intersection of psychological knowledge, ergonomics methods and big data creates an important new framework for driving new insights.

Item Type: Journal Item
Copyright Holders: 2015 Elsevier Ltd and The Ergonomics Society
ISSN: 0003-6870
Keywords: big data; leading indicators; human performance; methods
Academic Unit/School: Faculty of Arts and Social Sciences (FASS) > Psychology and Counselling > Psychology
Faculty of Arts and Social Sciences (FASS) > Psychology and Counselling
Faculty of Arts and Social Sciences (FASS)
Item ID: 46170
Depositing User: Ailsa Strathie
Date Deposited: 27 Apr 2016 12:19
Last Modified: 06 Aug 2019 08:48
URI: http://oro.open.ac.uk/id/eprint/46170
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