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Relevance research of threat/ error and undesired states in air traffic management based on Bayesian Network Model

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  • Zhao, Yifei
  • Hua, Shanshan
  • Ren, Xinhui

Abstract

We have realized that the quantitative analysis of safety data is an advanced technology of unsafely events research. Based on the analysis and statistical research on Air Traffic Control irregular events of 2011 using The Threat and Error Management (TEM) model, we have established the Bayesian network model to perform a precise quantitative analysis on the relevance between the threat, error and undesired states in Air Traffic Control operation. This analysis, based on the prior probability, obtained the relevance of the three kinds of safety information above through studying the respective posterior probabilities of threats or errors under undesired states. The result showed that the relevance of controller communication error and undesired states was 75%, and the relevance of Air Traffic Control threats as well as communication error with undesired states in Air Traffic Control was 13.3% and 25%, respectively. Therefore, this research method is of great significance for improving the mechanism of the Air Traffic Control operation risk management.

Suggested Citation

  • Zhao, Yifei & Hua, Shanshan & Ren, Xinhui, 2017. "Relevance research of threat/ error and undesired states in air traffic management based on Bayesian Network Model," Journal of Air Transport Management, Elsevier, vol. 60(C), pages 45-48.
    • Handle: RePEc:eee:jaitra:v:60:y:2017:i:c:p:45-48
    DOI: 10.1016/j.jairtraman.2017.01.001
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    References listed on IDEAS

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    1. Janic, Milan, 2000. "An assessment of risk and safety in civil aviation," Journal of Air Transport Management, Elsevier, vol. 6(1), pages 43-50.
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    Cited by:

    1. Wang, Yue & Wong, Collin Wai Hung & Cheung, Tommy King-Yin & Wu, Edmund Yangming, 2021. "How influential factors affect aviation networks: A Bayesian network analysis," Journal of Air Transport Management, Elsevier, vol. 91(C).

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