Change and safety: decision-making from data

The UK rail industry collects and analyses incident data to assess the risks experienced by passengers, the railway workforce and members of the public. The current analysis mainly compares current and historic incident rates and, for each type of incident, looks at the railway as a whole. However, changes to reduce risk are often made locally, at least at first, so a modelling approach is needed that is able to analyse local risk. In this paper the authors present a form of model that is able to make local risk estimates from incident data, using a case study – boarding and alighting incidents at stations. Using a Bayesian network (BN), the authors analyse the incident data with expert judgments about causal factors. The BN cannot be directly leant from data because the dataset contains no entries for the overwhelming majority of cases where the railway is used without incident. Instead, the incident data is analysed in the context of a model of the use of the railway, so that the prevalence of the causal factors in normal use can be compared with that in the incident data. The usage model is derived from multiple data sets; the use of a BN allows this to be done with approximations. Finally, the authors show how the model allows local risk to be estimated and consider the wider applicability of the techniques described.

• Record URL:
  • https://doi.org/10.1177/0954409713498381
• Record URL:
  • http://pif.sagepub.com/cgi/content/abstract/227/6/704
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/09544097
• Supplemental Notes:
  • Reprinted by permission of Sage Publications, Ltd.
• Authors:
  • Bearfield, George
  • Holloway, Anna
  • Marsh, William
• Publication Date: 2013-11

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: pp 704-714
• Serial:
  • Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit
  • Volume: 227
  • Issue Number: 6
  • Publisher: Sage Publications Limited
  • ISSN: 0954-4097
  • EISSN: 2041-3017
  • Serial URL: http://pif.sagepub.com/content/current

Subject/Index Terms

• TRT Terms: Bayes' theorem; Boarding and alighting; Case studies; Crash data; Decision making; Railroad safety; Risk assessment
• Geographic Terms: United Kingdom
• Subject Areas: Railroads; Safety and Human Factors; I80: Accident Studies;

Filing Info

• Accession Number: 01498693
• Record Type: Publication
• Files: TRIS
• Created Date: Nov 21 2013 9:07AM