 Relevance research of threat/ error and undesired states in air traffic management based on Bayesian Network ModelYifei Zhao, Shanshan Hua and Xinhui Ren Journal of Air Transport Management, 2017, vol. 60, issue C, 45-48 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. Keywords: Threat; Error; Undesired states; Bayesian network; Relevance (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:jaitra:v:60:y:2017:i:c:p:45-48 DOI: 10.1016/j.jairtraman.2017.01.001 Access Statistics for this article Journal of Air Transport Management is currently edited by Anne Graham More articles in Journal of Air Transport Management from Elsevier |
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