A Quantitative Study of the Occurrence of a Railway Accident Based on Belief Functions

  • Felipe Aguirre
  • Mohamed Sallak
  • Walter Schön
  • Fabien Belmonte
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 164)


In the field of railway systems, there is a great interest to include the human factor in the risk analysis process. Indeed, a great number of accidents are consider to be triggered by the human factors interacting in the situation. Several attempts have been made to include human factors in safety analysis, but they generally attack the problem in a qualitative way. The choice of qualitative methods arises from the difficulty to elicit human behavior and the effects on systems safety. This paper presents a first attempt to account for the human factor by using the generalized bayesian theory and fault tree analysis.


Epistemic Uncertainty Basic Belief Belief Function Fault Tree Analysis Conditional Belief 
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  1. 1.
    Hale, A.R., Hovden, J.: Management and culture: the third age of safety. A review of approaches to organizational aspects of safety, health and environment. In: Feyer, A.-M., Williamson, A. (eds.) Occupational Injury: Risk, Prevention, and Intervention, p. 129. Taylor & Francis (1998)Google Scholar
  2. 2.
    Swain, A., Guttmann, H.: Handbook of human-reliability analysis with emphasis on nuclear power plant applications. Final report, Sandia National Labs, Albuquerque, NM (USA). Tech. Rep. (1983)Google Scholar
  3. 3.
    Williams, J.: HEART, a proposed method for assessing and reducing human error (1986)Google Scholar
  4. 4.
    Kirwan, B.: A resources flexible approach to human reliability assessment for PRA. In: Safety and Reliability Symposium, pp. 114–135. Elsevier Inc., Altrincham (1990)Google Scholar
  5. 5.
    Reason, J.: Human Error. Cambridge University Press (1990)Google Scholar
  6. 6.
    Hollnagel, E.: Human reliability analysis: Context and control. Academic Press, London (1993)Google Scholar
  7. 7.
    Kirwan, B.: The validation of three human reliability quantification techniques THERP, HEART and JHEDI: Part I technique descriptions and validation issues. Applied Ergonomics 27(6), 359–373 (1996)CrossRefGoogle Scholar
  8. 8.
    Kirwan, B., Kennedy, R., Taylor-Adams, S., Lambert, B.: The validation of three Human Reliability Quantification techniques THERP, HEART and JHEDI: Part II Results of validation exercise. Applied Ergonomics 28(1), 17–25 (1997)CrossRefGoogle Scholar
  9. 9.
    Kirwan, B.: The validation of three human reliability quantification techniques THERP, HEART and JHEDI: Part III Practical aspects of the usage of the techniques. Applied Ergonomics 28(1), 27–39 (1997)CrossRefGoogle Scholar
  10. 10.
    Hovden, J., Albrechtsen, E., Herrera, I.A.: Is there a need for new theories, models and approaches to occupational accident prevention? Safety Science 48(8), 950–956 (2010)CrossRefGoogle Scholar
  11. 11.
    Belmonte, F., Schön, W., Heurley, L., Capel, R.: Interdisciplinary safety analysis of complex socio-technological systems based on the functional resonance accident model: An application to railway traffic supervision. Reliability Engineering & System Safety 96(2), 237–249 (2011)CrossRefGoogle Scholar
  12. 12.
    Utkin, L.V.: Imprecise Reliability: An Introductory Overview. In: Utkin, L.V., Coolen, F.P.A. (eds.) Computationa Intelligence in Reliability Engineering. SCI, vol. 40, pp. 261–306. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  13. 13.
    Tyagi, S.K., Pandey, D., Tyagi, R.: Fuzzy set theoretic approach to fault tree analysis. International Journal of Engineering, Science and Technology 2(5), 276–283 (2010)CrossRefGoogle Scholar
  14. 14.
    Zio, E., Marella, M., Podofillini, L.: A Monte Carlo simulation approach to the availability assessment of multi-state systems with operational dependencies. Reliability Engineering & System Safety 92(7), 871–882 (2007)CrossRefGoogle Scholar
  15. 15.
    Sallak, M., Schon, W., Aguirre, F.: Transferable belief model for reliability analysis of systems with data uncertainties and failure dependencies. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 224(4), 266–278 (2010), CrossRefGoogle Scholar
  16. 16.
    Aguirre, F., Sallak, M., Schon, W.: Generalized expressions of reliability of series-parallel and parallel-series systems using the Transferable Belief Model. In: Berenguer, C., Grall, A., Guedes Soares, C. (eds.) Advances in Safety, Reliability and Risk Management, September 2011, p. 344. Taylor & Francis, Troyes (2011)Google Scholar
  17. 17.
    Smets, P.: Belief functions: The disjunctive rule of combination and the generalized Bayesian theorem. International Journal of Approximate Reasoning 9(1), 1–35 (1993)MathSciNetzbMATHCrossRefGoogle Scholar
  18. 18.
    Sentz, K., Ferson, S.: Combination of Evidence in Dempster- Shafer Theory. SANDIA, Albuquerque, New Mexico. Tech. Rep (April 2002)Google Scholar
  19. 19.
    Parry, G.W.: Suggestions for an improved HRA method for use in probabilistic safety assessment. Reliability Engineering & System Safety 49(1), 1–12 (1995)CrossRefGoogle Scholar
  20. 20.
    Hollnagel, E., Green, M.: Accident Analysis and Barrier Functions. Institute for Energy Technology, Halden, Norway. Tech. Rep (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Felipe Aguirre
    • 1
  • Mohamed Sallak
    • 1
  • Walter Schön
    • 1
  • Fabien Belmonte
    • 2
  1. 1.Centre de recherche de RoyallieuUniversity of Technology of Compiègne. HEUDIASYC UMR 6599CompiègneFrance
  2. 2.Alstom TransportSaint-Ouen cedexFrance

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