三步法补充少报海上事故记录

IF 2.4 3区 工程技术 Q3 TRANSPORTATION Journal of Transportation Safety & Security Pub Date : 2021-06-02 DOI:10.1080/19439962.2021.1928353
Yao Yu, Guorong Li, Jinxian Weng
{"title":"三步法补充少报海上事故记录","authors":"Yao Yu, Guorong Li, Jinxian Weng","doi":"10.1080/19439962.2021.1928353","DOIUrl":null,"url":null,"abstract":"Abstract The underreporting issue on shipping accident data has plagued the researchers focused on maritime safety analysis for many years. For improving the quality of shipping accident records, this study proposes a novel methodology comprising three steps to complement the underreported maritime accident records. The first step is to investigate the underreporting rates under various conditions through questionnaire survey. Based on the survey results, the second step is to build a Cluster-Specific Random Effects (CSRE) model to estimate the underreporting rates under various scenarios. Then, the third step is to replicate the underreported accident records using the Monte Carlo simulation technique. Model results show that the occurrence probability of missing accident records involving liquid cargo ships is lower than other ship categories while fishing ships are more likely to have a higher underreporting rate. Non-serious accidents are more likely to be underreported than serious accidents. The case study confirms the effectiveness of the proposed three-step method for complementing the maritime accident databases suffering underreporting problems.","PeriodicalId":46672,"journal":{"name":"Journal of Transportation Safety & Security","volume":"37 1","pages":"1451 - 1469"},"PeriodicalIF":2.4000,"publicationDate":"2021-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"A three-step methodology to complement underreporting maritime accident records\",\"authors\":\"Yao Yu, Guorong Li, Jinxian Weng\",\"doi\":\"10.1080/19439962.2021.1928353\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract The underreporting issue on shipping accident data has plagued the researchers focused on maritime safety analysis for many years. For improving the quality of shipping accident records, this study proposes a novel methodology comprising three steps to complement the underreported maritime accident records. The first step is to investigate the underreporting rates under various conditions through questionnaire survey. Based on the survey results, the second step is to build a Cluster-Specific Random Effects (CSRE) model to estimate the underreporting rates under various scenarios. Then, the third step is to replicate the underreported accident records using the Monte Carlo simulation technique. Model results show that the occurrence probability of missing accident records involving liquid cargo ships is lower than other ship categories while fishing ships are more likely to have a higher underreporting rate. Non-serious accidents are more likely to be underreported than serious accidents. The case study confirms the effectiveness of the proposed three-step method for complementing the maritime accident databases suffering underreporting problems.\",\"PeriodicalId\":46672,\"journal\":{\"name\":\"Journal of Transportation Safety & Security\",\"volume\":\"37 1\",\"pages\":\"1451 - 1469\"},\"PeriodicalIF\":2.4000,\"publicationDate\":\"2021-06-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Transportation Safety & Security\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1080/19439962.2021.1928353\",\"RegionNum\":3,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"TRANSPORTATION\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Transportation Safety & Security","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1080/19439962.2021.1928353","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"TRANSPORTATION","Score":null,"Total":0}
引用次数: 3

摘要

摘要多年来,船舶事故数据少报问题一直困扰着从事海上安全分析的研究人员。为了提高船舶事故记录的质量,本研究提出了一种新的方法,包括三个步骤来补充少报的海上事故记录。第一步是通过问卷调查的方式调查不同情况下的漏报率。基于调查结果,第二步是建立集群特定随机效应(Cluster-Specific Random Effects, CSRE)模型,估算不同情景下的漏报率。然后,第三步是使用蒙特卡罗模拟技术复制少报的事故记录。模型结果表明,液货船漏报事故记录的概率低于其他船舶类别,而渔船漏报的概率更高。非严重事故比严重事故更容易被漏报。案例研究证实了拟议的三步法在补充存在漏报问题的海上事故数据库方面的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
A three-step methodology to complement underreporting maritime accident records
Abstract The underreporting issue on shipping accident data has plagued the researchers focused on maritime safety analysis for many years. For improving the quality of shipping accident records, this study proposes a novel methodology comprising three steps to complement the underreported maritime accident records. The first step is to investigate the underreporting rates under various conditions through questionnaire survey. Based on the survey results, the second step is to build a Cluster-Specific Random Effects (CSRE) model to estimate the underreporting rates under various scenarios. Then, the third step is to replicate the underreported accident records using the Monte Carlo simulation technique. Model results show that the occurrence probability of missing accident records involving liquid cargo ships is lower than other ship categories while fishing ships are more likely to have a higher underreporting rate. Non-serious accidents are more likely to be underreported than serious accidents. The case study confirms the effectiveness of the proposed three-step method for complementing the maritime accident databases suffering underreporting problems.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
6.00
自引率
15.40%
发文量
38
期刊最新文献
Examining the crash risk factors associated with cycling by considering spatial and temporal disaggregation of exposure: Findings from four Dutch cities Traffic safety performance evaluation in a connected vehicle environment with queue warning and speed harmonization applications Enhancing bicyclist survival time in fatal crashes: Investigating the impact of faster crash notification time through explainable machine learning Factors affecting pedestrian injury severity in pedestrian-vehicle crashes: Insights from a data mining and mixed logit model approach Prediction of high-risk bus drivers characterized by aggressive driving behavior
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1