Article(id=1240655019859571567, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1240655017040991028, articleNumber=null, orderNo=null, doi=10.3969/j.issn.1000-4653.2025.04.006, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1730476800000, receivedDateStr=2024-11-02, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773724806989, onlineDateStr=2026-03-17, pubDate=1766592000000, pubDateStr=2025-12-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773724806989, onlineIssueDateStr=2026-03-17, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773724806989, creator=13701087609, updateTime=1773724806989, updator=13701087609, issue=Issue{id=1240655017040991028, tenantId=1146029695717560320, journalId=1240618002186551303, year='2025', volume='48', issue='4', pageStart='1', pageEnd='199', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773724806318, creator=13701087609, updateTime=1773725219619, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1240656750618145316, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1240655017040991028, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1240656750618145317, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1240655017040991028, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=47, endPage=58, ext={EN=ArticleExt(id=1240655020107035508, articleId=1240655019859571567, tenantId=1146029695717560320, journalId=1240618002186551303, language=EN, title=An emergency decision-making method for Ro-Ro passenger ship collisions based on fuzzy Bayesian network, columnId=1240655017900823355, journalTitle=Navigation of China, columnName=Marine Traffic Safety, runingTitle=null, highlight=null, articleAbstract=

As an important component of the waterway transportation system, Ro-Ro passenger ship transportation plays a significant role in inland river, coastal, and even cross-strait transport services. In recent years, collisions involving Ro-Ro passenger ships have occurred from time to time. To mitigate the losses caused by such accidents, this paper proposes an emergency decision-making model for Ro-Ro passenger ship collisions based on a fuzzy Bayesian network. The identified emergency decision variables for RoPax ship collisions are fuzzified by introducing fuzzy logic. Combined with improved IF-THEN rules, confidence rule bases are established and then converted into a conditional probability table, thereby constructing a complete Bayesian network inference structure. Ultimately, the optimal emergency decision scheme is determined through utility value evaluation. The results demonstrate that the proposed emergency decision-making model is effective and feasible, aligning with practical application requirements. This study provides ship decision-makers with a reference basis for emergency response in the event of a RoPax ship collision.

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客滚运输作为水路交通系统的重要组成部分，在内河、沿海甚至跨海峡之间的水路运输服务中扮演着重要角色。近年来，客滚船碰撞事故时有发生，为降低因碰撞造成的损失，提出基于模糊贝叶斯网络的客滚船碰撞应急决策模型。通过引入模糊逻辑对确定的客滚船碰撞应急决策变量进行定量或定性的模糊化处理，并结合改进的IF-THEN规则获得置信规则库，再将其转换为条件概率表，从而建立完整的贝叶斯网络推理结构，最终通过效用值确定最优应急决策方案。案例验证的结果表明所提出的应急决策模型有效可行，且符合实际应用场景。研究成果为船舶决策者在发生客滚船碰撞事故时提供应急决策的判断依据。

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陈厚忠（1976—），男，副教授，博士，研究方向为海事安全与保障。E-mail:chzwhut@163.com

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陈厚忠（1976—），男，副教授，博士，研究方向为海事安全与保障。E-mail:chzwhut@163.com

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