Skip to main content

Generalising Event Trees Using Bayesian Networks with a Case Study of Train Derailment

  • Conference paper
Computer Safety, Reliability, and Security (SAFECOMP 2005)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 3688))

Included in the following conference series:

Abstract

Event trees are a popular technique for modelling accidents in system safety analyses. Bayesian networks are a probabilistic modelling technique representing influences between uncertain variables. Although popular in expert systems, Bayesian networks are not used widely for safety. Using a train derailment case study, we show how an event tree can be viewed as a Bayesian network, making it clearer when one event affects a later one. Since this effect needs to be understood to construct an event tree correctly, we argue that the two notations should be used together. We then show how the Bayesian Network enables the factors that influence the outcome of events to be represented explicitly. In the case study, this allowed the train derailment model to be generalised and applied in more circumstances. Although the resulting model is no longer just an event tree, the familiar event tree notation remains useful.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. US Nuclear Regulatory Commission: Reactor Safety Study. WASH-1400, NUREG 75/014 (1975)

    Google Scholar 

  2. Jensen, F.V.: An Introduction to Bayesian Networks. UCL Press, London (1996)

    Google Scholar 

  3. Neil, M., Fenton, N.: Building Large Scale Bayesian Networks. Knowledge Engineering Review 15(3), 257–284 (2000)

    Article  MATH  Google Scholar 

  4. Neil, M., Fenton, N., Forey, S., Harris, R.: Using Bayesian Belief Networks to Predict the Reliability of Military Vehicles. IEE Computing and Control Engineering J 12(1), 11–20 (2001)

    Article  Google Scholar 

  5. Fenton, N.E., Krause, P., Neil, M.: Software Measurement: Uncertainty and Causal Modelling. IEEE Software 10(4), 116–122 (2002)

    Article  Google Scholar 

  6. The Safety and Standards Directorate: Safety Risk Model (SRM), Report No. SP-RSK-3.1.3.8, Rail Safety and Standards Board, UK (1999)

    Google Scholar 

  7. Bedford, T., French, S., Quigley, J.: Statistical Review Of The Safety Risk Model (WP1), Report T127 (WP1), Rail Safety and Standards Board, UK (2004)

    Google Scholar 

  8. Neil, M., Malcolm, B., Shaw, R.: Modelling an Air Traffic Control Environment Using Bayesian Belief Networks. In: Proc. 21st Systems Safety Conference, Ottawa, ISBN 0-9721385-2-8

    Google Scholar 

  9. Vatn, J., Svee, H.A.: Risk Based Approach to Determine Ultrasonic Inspection Frequencies in Railway Applications. In: Proceedings of the 22nd ESReDA Seminar, Madrid, Spain, May 27-28 (2002)

    Google Scholar 

  10. Roelen, A.L.C., Wever, R., Cooke, R.M., Lopuhaä, H.P., Hale, A.R., Goossens, L.H.J.: Aviation Causal Model using Bayesian Belief Nets to Quantify Management Influence. In: van Gelder, B. (ed.) Safety and Reliability, Swets & Zeitlinger, Lisse, pp. 1315–1320 (2003), ISBN 90 5809 551 7

    Google Scholar 

  11. Zavisca, M., Kahlert, H., Khatib-Rahbar, M., Grindon, E., Ang, M.: A Bayesian Network Approach to Accident Management and Estimation of Source Terms for Emergency Planning. In: Proceedings of PSAM7/ESREL 2004. Springer, Heidelberg (2004)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Bearfield, G., Marsh, W. (2005). Generalising Event Trees Using Bayesian Networks with a Case Study of Train Derailment. In: Winther, R., Gran, B.A., Dahll, G. (eds) Computer Safety, Reliability, and Security. SAFECOMP 2005. Lecture Notes in Computer Science, vol 3688. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11563228_5

Download citation

  • DOI: https://doi.org/10.1007/11563228_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-29200-5

  • Online ISBN: 978-3-540-32000-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics