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Maritime Transportation Risk Assessment of Tianjin Port with Bayesian Belief Networks

机译：贝叶斯网络在天津港海上运输风险评估中的应用

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摘要

This article develops a Bayesian belief network model for the prediction of accident consequences in the Tianjin port. The study starts with a statistical analysis of historical accident data of six years from 2008 to 2013. Then a Bayesian belief network is constructed to express the dependencies between the indicator variables and accident consequences. The statistics and expert knowledge are synthesized in the Bayesian belief network model to obtain the probability distribution of the consequences. By a sensitivity analysis, several indicator variables that have influence on the consequences are identified, including navigational area, ship type and time of the day. The results indicate that the consequences are most sensitive to the position where the accidents occurred, followed by time of day and ship length. The results also reflect that the navigational risk of the Tianjin port is at the acceptable level, despite that there is more room of improvement. These results can be used by the Maritime Safety Administration to take effective measures to enhance maritime safety in the Tianjin port.
机译：本文建立了贝叶斯信念网络模型，用于预测天津港的事故后果。该研究从对2008年至2013年的六年历史事故数据进行统计分析开始。然后构建贝叶斯信念网络来表达指标变量与事故后果之间的依存关系。在贝叶斯信念网络模型中综合统计数据和专家知识，以获得后果的概率分布。通过敏感性分析，确定了对后果有影响的几个指标变量，包括航行区域，船舶类型和一天中的时间。结果表明，后果对事故发生的位置最敏感，其次是时间和船长。结果还表明，尽管还有更多改进的余地，天津港的航行风险仍处于可接受的水平。海事局可以利用这些结果采取有效措施，加强天津港的海上安全。

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