全球集装箱船航运死亡事故的关键因素和热点研究

doi:10.16265/j.cnki.issn1003-3033.2022.11.2700

中国安全科学学报 ›› 2022, Vol. 32 ›› Issue (11): 154-159.doi: 10.16265/j.cnki.issn1003-3033.2022.11.2700

全球集装箱船航运死亡事故的关键因素和热点研究

汪金辉¹^,²(), 周雨¹(), 庄磊³, 张睿卿¹

¹ 上海海事大学海洋科学与工程学院,上海 201306
² 上海海事大学安全与防护技术研究中心,上海 201306
³ 中国船级社上海规范研究所,上海 200135

收稿日期:2022-06-10 修回日期:2022-09-17 出版日期:2022-11-28 发布日期:2023-05-28
作者简介:
汪金辉 (1981—),男,安徽桐城人,博士,副教授,博士生导师,主要从事船舶、海洋平台火灾安全方面的研究。E-mail:jhwang217@126.com。
周雨(1995—),男,安徽宿州人,博士研究生,研究方向为海事事故大数据挖掘与海上风险评估。E-mail:2410788918@qq.com。
庄磊, 高级工程师
基金资助:
国家重点研发计划(2021YFC2801005)

Research on key factors and hot spots of global container shipping fatal accidents

WANG Jinhui¹^,²(), ZHOU Yu¹(), ZHUANG Lei³, ZHANG Ruiqing¹

¹ College of Ocean Science and Engineering, Shanghai Maritime University, Shanghai 201306, China
² Research Center of Safety and Protection Technology, Shanghai Maritime University, Shanghai 201306, China
³ Shanghai Rules & Research Institute, China Classification Society, Shanghai 200135, China

Received:2022-06-10 Revised:2022-09-17 Online:2022-11-28 Published:2023-05-28

摘要/Abstract

摘要：

为识别集装箱船航运过程中死亡事故的关键因素及其空间热点,根据IHS Sea-web数据库中1990—2015年全球集装箱船事故信息,采用零膨胀负二项(ZINB)回归模型,评估不同事故因素对船员死亡的影响程度,应用核密度估计法可视化不同事故因素的空间热点分布。结果表明:碰撞和船体/机械损坏事故的频率最高,分别为32.9%和31.3%,沉没事故的频率最低(0.8%);集装箱船航运事故数据呈现显著的零膨胀现象,采用ZINB回归模型能够有效挖掘死亡事故的关键因素,其中沉没和火灾/爆炸事故的致死率最高,分别为0.546和0.348,其次为碰撞(0.216)、触碰(0.127)和船体/机械损坏(0.004)事故;集装箱船航运死亡事故的空间热点分布在欧洲、中国、日本、韩国、地中海、苏伊士运河、马六甲海峡和新加坡海峡等这些传统的船舶事故热点水域,此外,发现新增圣劳伦斯河和萨利什海2个热点地区。ZINB回归模型和核密度估计法能分别有效识别航运死亡事故的关键因素和热点,值得在海事事故的研究中推广应用。

关键词: 集装箱船航运, 死亡事故, 关键因素, 热点分布, 零膨胀负二项(ZINB)回归, 核密度估计

Abstract:

To identify the key factors and hot spots of fatal accidents during container ship shipping, using global container ship accident data from 1990 to 2015 in Information Handling Services (IHS) Sea-web database, ZINB regression model was used to evaluate the impacts of multiple accident factors on crew deaths, and then kernel density estimation was applied to visualize the hot spots of different accident factors. The findings show that collision and hull/machinery damage accidents have the highest frequency, with 32.9% and 31.3%, respectively, and foundered accidents have the lowest frequency (0.8%). The container ship shipping accident data exhibits a significant zero-expansion phenomenon, and the ZINB regression model can effectively analyze the key factors of fatal accidents. Foundered and fire/explosion accidents have the highest fatality rates, with 0.546 and 0.348, respectively, followed by collision (0.216), contact (0.127), and hull/machinery damage (0.004) accidents. The hot spots of the key factors of container ship shipping fatalities are distributed in Europe, China, Japan, Korea, the Mediterranean Sea, the Suez Canal, the Malacca Strait, and the Singapore Strait, which are traditional hot spots for ship accidents. In addition, the St. Lawrence River and the Salish Sea are newly discovered hot spots. The ZINB regression model and the kernel density estimation method can effectively identify the key factors and hotspots of shipping fatalities, respectively, and are worthy of popularization and application in maritime accident research.

Key words: container ship, fatal accidents, key factors, hot spot distribution, zero-inflated negative binomial (ZINB) regression, kernel density estimation

汪金辉, 周雨, 庄磊, 张睿卿. 全球集装箱船航运死亡事故的关键因素和热点研究[J]. 中国安全科学学报, 2022, 32(11): 154-159.

WANG Jinhui, ZHOU Yu, ZHUANG Lei, ZHANG Ruiqing. Research on key factors and hot spots of global container shipping fatal accidents[J]. China Safety Science Journal, 2022, 32(11): 154-159.

图/表 4

参考文献 20

[1]	DUCRUET C, NOTTEBOOM T. The worldwide maritime network of container shipping:spatial structure and regional dynamics[J]. Global Networks, 2012, 12(3):395-423. doi: 10.1111/j.1471-0374.2011.00355.x
[2]	CHEN Jihong, BIAN Wentao, WAN Zheng, et al. Identifying factors influencing total-loss marine accidents in the world:analysis and evaluation based on ship types and sea regions[J]. Ocean Engineering, 2019,191:DOI:10.1016/j.oceaneng.2019.106495. doi: 10.1016/j.oceaneng.2019.106495
[3]	ZHENG Yishu, TALLEY W K, JIN Di, et al. Crew injuries in container vessel accidents[J]. Maritime Policy & Management, 2016, 43(5):541-551.
[4]	OZTURK U, CICEK K. Individual collision risk assessment in ship navigation:a systematic literature review[J]. Ocean Engineering, 2019, 180:130-143. doi: 10.1016/j.oceaneng.2019.03.042
[5]	付姗姗, 刘燕平, 席永涛, 等. 北极海冰环境下船舶事故严重程度影响因素分析[J]. 中国安全科学学报, 2019, 29(10):84-90. doi: 10.16265/j.cnki.issn1003-3033.2019.10.013
	FU Shanshan, LIU Yanping, XI Yongtao, et al. Analysis of influencing factors on severity of ship accidents in ice-covered Arctic waters[J]. China Safety Science Journal, 2019, 29(10):84-90. doi: 10.16265/j.cnki.issn1003-3033.2019.10.013
[6]	TALLEY W K, JIN Di, KITE-POWELL H. Determinants of the severity of passenger vessel accidents[J]. Maritime Policy & Management, 2006, 33(2):173-186.
[7]	YIP T L. Port traffic risks:a study of accidents in Hong Kong waters[J]. Transportation Research Part E:Logistics and Transportation Review, 2008, 44(5):921-931. doi: 10.1016/j.tre.2006.09.002
[8]	WENG Jinxian, YANG Dong. Investigation of shipping accident injury severity and mortality[J]. Accident Analysis & Prevention, 2015, 76:92-101. doi: 10.1016/j.aap.2015.01.002
[9]	王昊, 杨亚东, 马勇, 等. 基于零膨胀负二项回归模型的内河水上交通人命安全分析[J]. 安全与环境学报, 2017, 17(2):408-412.
	WANG Hao, YANG Yadong, MA Yong, et al. Safety analysis over the casualties of maritime transportation and traffic accidents based on the zero-inflated negative binomial regression method[J]. Journal of Safety and Environment, 2017, 17(2):408-412.
[10]	WENG Jinxian, GE Yingen, HAN Hao. Evaluation of shipping accident casualties using zero-inflated negative binomial regression technique[J]. The Journal of Navigation, 2016, 69(2):433-448. doi: 10.1017/S0373463315000788
[11]	WANG Jinhui, ZHOU Yu, ZHANG Shaogang, et al. Societal risk acceptance criteria of the global general cargo ships[J]. Ocean Engineering, 2022, 261:DOI:10.1016/j.oceaneng.2022.112162. doi: 10.1016/j.oceaneng.2022.112162
[12]	ACHARYA T D, YOO K W, LEE D H. GIS-based spatio-temporal analysis of marine accidents database in the coastal zone of Korea[J]. Journal of Coastal Research, 2017, 79:114-118. doi: 10.2112/SI79-024.1
[13]	ZHANG Yang, SUN Xukai, CHEN Jihong, et al. Spatial patterns and characteristics of global maritime accidents[J]. Reliability Engineering & System Safety, 2021, 206:DOI:10.1016/j.ress.2020.107310. doi: 10.1016/j.ress.2020.107310
[14]	WANG Jinhui, ZHOU Yu, ZHUANG Lei, et al. Study on the critical factors and hot spots of crude oil tanker accidents[J]. Ocean & Coastal Management, 2022, 217:DOI:10.1016/j.ocecoaman.2021.106010. doi: 10.1016/j.ocecoaman.2021.106010
[15]	刘振博. 基于ZINB及Tobit回归的高速公路交通事故预测模型研究[D]. 哈尔滨: 哈尔滨工业大学, 2019.
	LIU Zhenbo. Research on prediction models of freeway traffic accidents based on ZINB and Tobit regression[D]. Harbin: Harbin Institute of Technology, 2019.
[16]	司东森, 张英俊, 郎坤. 基于改进BN的集装箱船舶碰撞事故致因分析[J]. 中国安全科学学报, 2019, 29(10):31-37. doi: 10.16265/j.cnki.issn1003-3033.2019.10.006
	SI Dongsen, ZHANG Yingjun, LANG Kun, et al. Causation analysis of container ship collision accidents based on improved BN[J]. China Safety Science Journal, 2019, 29(10):31-37. doi: 10.16265/j.cnki.issn1003-3033.2019.10.006
[17]	盛进路, 刘柱. 船舶设备事故中的人为因素分析与控制研究[J]. 中国安全科学学报, 2009, 19(3):39-44.
	SHENG Jinlu, LIU Zhu. Analysis and control of human factors in ship equipment accidents[J]. China Safety Science Journal, 2009, 19(3):39-44.
[18]	SIRIMANNE S N, JUAN W, ASARIOTIS R, et al. Review of maritime transport[R]. United Nations Conference on Trade and Development, 2020.
[19]	WU Lin, XU Yongjun, WANG Qi, et al. Mapping global shipping density from AIS data[J]. The Journal of Navigation, 2017, 70(1):67-81. doi: 10.1017/S0373463316000345
[20]	黄道正. 水上交通事故的空间分布与后果评级研究[D]. 上海: 上海交通大学, 2015.
	HUANG Daozheng. Study on spatial distribution and consequence classification of maritime traffic accidents[D]. Shanghai: Shanghai Jiaotong University, 2015.

因素/参数	系数	标准差		统计量		p值		95%置信区间
碰撞	2.859	1.095		2.61		0.009		(0.71, 5.00)
触碰	1.688	1.286		1.31		0.189		(-0.83, 4.20)
火灾/爆炸	4.609	1.157		3.98		0		(2.34, 6.87)
沉没	7.22		1.827		3.95		0		(3.63, 10.80)
船体/机械损坏	0.051		1.294		0.04		0.968		(-2.48, 2.58)
离散参数a	42.003		10.216		—		—		(26.07, 67.65)

全球集装箱船航运死亡事故的关键因素和热点研究

Research on key factors and hot spots of global container shipping fatal accidents

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