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A Benefit/Cost/Deficit (BCD) model for learning from human errors

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  • Vanderhaegen, Frédéric
  • Zieba, Stéphane
  • Enjalbert, Simon
  • Polet, Philippe

Abstract

This paper proposes an original model for interpreting human errors, mainly violations, in terms of benefits, costs and potential deficits. This BCD model is then used as an input framework to learn from human errors, and two systems based on this model are developed: a case-based reasoning system and an artificial neural network system. These systems are used to predict a specific human car driving violation: not respecting the priority-to-the-right rule, which is a decision to remove a barrier. Both prediction systems learn from previous violation occurrences, using the BCD model and four criteria: safety, for identifying the deficit or the danger; and opportunity for action, driver comfort, and time spent; for identifying the benefits or the costs. The application of learning systems to predict car driving violations gives a rate over 80% of correct prediction after 10 iterations. These results are validated for the non-respect of priority-to-the-right rule.

Suggested Citation

  • Vanderhaegen, Frédéric & Zieba, Stéphane & Enjalbert, Simon & Polet, Philippe, 2011. "A Benefit/Cost/Deficit (BCD) model for learning from human errors," Reliability Engineering and System Safety, Elsevier, vol. 96(7), pages 757-766.
    • Handle: RePEc:eee:reensy:v:96:y:2011:i:7:p:757-766
    DOI: 10.1016/j.ress.2011.02.002
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    References listed on IDEAS

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    1. Reer, Bernhard, 2008. "Review of advances in human reliability analysis of errors of commission—Part 2: EOC quantification," Reliability Engineering and System Safety, Elsevier, vol. 93(8), pages 1105-1122.
    2. Reer, Bernhard, 2008. "Review of advances in human reliability analysis of errors of commission, Part 1: EOC identification," Reliability Engineering and System Safety, Elsevier, vol. 93(8), pages 1091-1104.
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    Cited by:

    1. Xu, Zhaoyi & Saleh, Joseph Homer, 2021. "Machine learning for reliability engineering and safety applications: Review of current status and future opportunities," Reliability Engineering and System Safety, Elsevier, vol. 211(C).
    2. Dingan Ni & Fengxiang Guo & Hui Zhang & Mingyuan Li & Yanning Zhou, 2022. "Improving Older Drivers’ Behaviors Using Theory of Planned Behavior," Sustainability, MDPI, vol. 14(8), pages 1-16, April.

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