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When non-significance may be significant : lessons learned from a study of the development, implementation and evaluation of a fleet risk assessment tool

Wishart, Darren E., Freeman, James E., Davey, Jeremy D., Wilson, Adrian, & Rowland, Bevan D. (2012) When non-significance may be significant : lessons learned from a study of the development, implementation and evaluation of a fleet risk assessment tool. In Dorn, Lisa (Ed.) Driver Behaviour and Training. Ashgate, Surrey, pp. 197-214.

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    Abstract

    This paper reports on the development and implementation of a self-report risk assessment tool that was developed in an attempt to increase the efficacy of crash prediction within Australian fleet settings. This study forms a part of a broader program of research into work related road safety and identification of driving risk. The first phase of the study involved a series of focus groups being conducted with 217 professional drivers which revealed that the following factors were proposed to influence driving performance: Fatigue, Knowledge of risk, Mood, Impatience and frustration, Speed limits, Experience, Other road users, Passengers, Health, and Culture. The second phase of the study involved piloting the newly developed 38 item Driving Risk Assessment Scale - Work Version (DRAS-WV) with 546 professional drivers. Factor analytic techniques identified a 9 factor solution that was comprised of speeding, aggression, time pressure, distraction, casualness, awareness, maintenance, fatigue and minor damage. Speeding and aggressive driving manoeuvres were identified to be the most frequent aberrant driving behaviours engaged in by the sample. However, a series of logistic regression analyses undertaken to determine the DRAS-WV scale’s ability to predict self-reported crashes revealed limited predictive efficacy e.g., 10% of crashes. This paper outlines proposed reasons for this limited predictive ability of the DRAS-WV as well as provides suggestions regarding the future of research that aims to develop methods to identify “at risk” drivers.

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    ID Code: 53468
    Item Type: Book Chapter
    Keywords: risk assessment tool, crash prediction
    ISBN: 9781409443049
    Subjects: Australian and New Zealand Standard Research Classification > PSYCHOLOGY AND COGNITIVE SCIENCES (170000)
    Divisions: Current > Research Centres > Centre for Accident Research & Road Safety - Qld (CARRS-Q)
    Current > QUT Faculties and Divisions > Faculty of Health
    Current > Institutes > Institute of Health and Biomedical Innovation
    Current > Schools > School of Psychology & Counselling
    Copyright Owner: Copyright 2012 Ashgate Publishing
    Deposited On: 05 Sep 2012 08:14
    Last Modified: 22 May 2013 16:59

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