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A comparison of data-driven and model-based approaches to quantifying railway risk

Bedford, T.J. and Quigley, J.L. and French, S. (2004) A comparison of data-driven and model-based approaches to quantifying railway risk. In: Probabilistic Safety Assessment and Management. Springer-Verlag, London, pp. 2765-2771. ISBN 9781852338275

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    This paper presents some of the results of a project sponsored by the UK Railway Safety and Standards Board (RSSB). An earlier statistical evaluation of a previous version of the RSSB Safety Risk Model (SRM), a combined Fault/Event Tree, conducted by Prof Andrew Evans had concluded that the model was unduly pessimistic. We have constructed a hypothesis test based on the relative likelihood techniques using the most recent version of the SRM as the null hypothesis. The results support the SRM being consistent with the historical data. Two significant differences between these two studies are the statistical methods employed to support the analysis and the removal of certain significant conservative assumptions from updating the versions of the SRM. The paper discusses the demands that different model purposes place on these models, and explores the question of whether or not it is meaningful to compare their outputs. The use of expected fatalities as a metric for expressing risk in both models is questioned because of the heavy-tailed form of the distribution for fatality numbers given a fatal accident.

    Item type: Book Section
    ID code: 9637
    Notes: Also presented at: International Conference on Probabalistic Safety Assessment and Management (PSAM), Hotel Inter-Continental Berlin, Germany, 14-18 June, 2004.
    Keywords: UK Railway Safety and Standards Board, reliability, system performance, Safety Risk Model, RSSB, Management. Industrial Management, Transportation and Communications, Probabilities. Mathematical statistics, Safety, Risk, Reliability and Quality, Management Science and Operations Research
    Subjects: Social Sciences > Industries. Land use. Labor > Management. Industrial Management
    Social Sciences > Transportation and Communications
    Science > Mathematics > Probabilities. Mathematical statistics
    Department: Strathclyde Business School > Management Science
    Related URLs:
    Depositing user: Strathprints Administrator
    Date Deposited: 18 Mar 2010 10:36
    Last modified: 30 Jul 2014 07:01

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