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

机译：基于贝叶斯网络模型的空中交通管理中威胁/错误与不良状态的相关性研究

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摘要

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. (C) 2017 Elsevier Ltd. All rights reserved.
机译：我们已经意识到安全数据的定量分析是不安全事件研究的一项先进技术。在使用威胁和错误管理（TEM）模型对2011年空中交通管制违规事件进行分析和统计研究的基础上，我们建立了贝叶斯网络模型以对威胁，错误和不良状态之间的相关性进行精确的定量分析。在空中交通管制操作中。该分析基于先验概率，通过研究不期望状态下威胁或错误的各个后验概率，获得了上述三种安全信息的相关性。结果表明，管制员通信错误和不良状态的相关性为75％，空中交通管制威胁与不良状态中的通信错误的相关性分别为13.3％和25％。因此，该研究方法对完善空中交通管制运行风险管理机制具有重要意义。 （C）2017 Elsevier Ltd.保留所有权利。

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