基于贝叶斯网络的港口国动态检查有效性研究
A Study on Effectiveness of Dynamic Port State Control Inspection based on Bayesian Network
-
摘要: 港口国监控(PSC)检查是提高船舶安全水平,降低船舶事故率的重要措施。该研究利用贝叶斯网络模型,结合英国劳氏船级社(LR)的全球船舶数据、国际海事组织(IMO)的事故数据,以及东京、巴黎、印度三大MoU (Memorandum of Understanding)的PSC检查数据,并以船舶事故率代表船舶风险水平,探究PSC检查次数对船舶风险水平的影响,从而研究各MoU的PSC动态检查有效性。研究结果表明,对于Tokyo MoU,随检查次数的增加船舶事故率先升高后降低,而其他两个MoU对船舶增加检查未能降低事故率。因此建议Tokyo MoU对于某些船舶可适当增加检查次数以降低船舶事故风险,而另外两个MoU的动态检查有效性仍需进一步提高。除此之外,可依据对船舶风险的预测有效选择目标船舶,提高港口国检查效率。Abstract: The Bayesian Network model is used to explore the relationship between times of port state control inspection and ship accidents. The data mainly include ship data from Lloyd’s Register of Shipping(LR), accident data from International Maritime Organization(IMO) and PSC inspection data of three MoUs including Tokyo MoU, Paris MoU and India MoU. The research indicates that for Tokyo, accident rate goes up first then down as the times of inspection increase. However, for other two countries, increase of inspect times has little effect on the accident rate, suggesting inefficient of the inspections there.