A Cause Analysis of Extraordinarily Severe Traffic Crashes Based on T-S Fuzzy Fault Tree and Bayesian Network

ZHENG Lai; GU Peng; LU Jian

doi:10.3963/j.jssn.1674-4861.2021.04.006

Volume 39 Issue 4

Aug. 2021

Turn off MathJax

Article Contents

Article Navigation > Journal of Transport Information and Safety > 2021 > 39(4): 43-51+59

ZHENG Lai, GU Peng, LU Jian. A Cause Analysis of Extraordinarily Severe Traffic Crashes Based on T-S Fuzzy Fault Tree and Bayesian Network[J]. Journal of Transport Information and Safety, 2021, 39(4): 43-51+59. doi: 10.3963/j.jssn.1674-4861.2021.04.006

Citation:

ZHENG Lai, GU Peng, LU Jian. A Cause Analysis of Extraordinarily Severe Traffic Crashes Based on T-S Fuzzy Fault Tree and Bayesian Network[J]. Journal of Transport Information and Safety, 2021, 39(4): 43-51+59. doi: 10.3963/j.jssn.1674-4861.2021.04.006

Citation:

PDF( 1835 KB)

A Cause Analysis of Extraordinarily Severe Traffic Crashes Based on T-S Fuzzy Fault Tree and Bayesian Network

doi: 10.3963/j.jssn.1674-4861.2021.04.006

ZHENG Lai^{1
,
,},
GU Peng¹,
LU Jian²

1.
School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin 150090, China
2.
Traffic Management Research Institute of the Ministry of Public Security, Wuxi 214151, Jiangsu, China

Abstract

Abstract

The T-S fuzzy fault tree and Bayesian network are integrated for an in-depth analysis to identify the main causes of extraordinarily severe traffic crashes. A T-S fuzzy fault tree is established, with the extraordinarily severe traffic crash taken as the top event, the human, vehicle, road, and environmental factors taken as the intermediate events, and 24 sub-factors taken as the basic events. The fuzzy fault tree is transformed into a Bayesian network, and the importance and posterior probability of the basic events can be inferred biaxially to determine the main causes. The results show that the method of fusing T-S fuzzy fault tree and Bayesian network can improve the accuracy and reliability of the analysis results of the causes of extraordinarily severe traffic crashes through forward and reverse reasoning and can determine improper operation, speeding, imperfect protection facilities, and bending. Slope combination, slippery road surface, and failure to drive following regulations are the six major causes of extraordinarily severe traffic crashes. The six major causes are analyzed, revealing that improper operation and speeding are more critical for extraordinarily severe traffic crashes.
- traffic safety,
- extraordinarily severe traffic crashes,
- cause analysis of crashes,
- T-S fuzzy fault tree,
- Bayesian network

FullText(HTML)

References(20)

References

[1]	孟祥海. 道路交通安全技术与实践案例[M]. 北京: 人民交通出版社, 2017. MENG Xianghai. Road traffic safety technology and practice cases[M]. Beijing: People's Communications Press, 2017. (in Chinese)
[2]	许得杰, 钱勇生, 王敏, 等. 甘肃省重特大道路交通事故特征分析[J]. 中国公共安全(学术版), 2011(4): 100-103. doi: 10.3969/j.issn.1672-2396.2011.04.026 XU Dejie, QIAN Yongsheng, WANG Min, et al. Analysis of characteristics of major road traffic accidents in Gansu province[J]. China Public Safety (Academic Edition), 2011(4): 100-103. (in Chinese) doi: 10.3969/j.issn.1672-2396.2011.04.026
[3]	袁泉, 李一兵, 陈康. 引发重大交通事故的显著因素特点分析及安全对策[J]. 中国司法鉴定, 2015(5): 34-40. doi: 10.3969/j.issn.1671-2072.2015.05.006 YUAN Quan, LI Yibing, CHEN Kang. Analysis of the characteristics of the significant factors leading to major traffic accidents and safety countermeasures[J]. China Forensic Expertise, 2015(5): 34-40. (in Chinese) doi: 10.3969/j.issn.1671-2072.2015.05.006
[4]	XU Chengcheng, BAO Jie, WANG Chen, et al. Association rule analysis of factors contributing to extraordinarily severe traffic crashes in China[J]. Journal of Safety Research, 2018(67): 65-75. http://www.onacademic.com/detail/journal_1000040875882610_2077.html
[5]	ZENG Qiang, GU Weihua, ZHANG Xuan, et al. Analyzing freeway crash severity using a Bayesian spatial generalized ordered logit model with conditional autoregressive priors[J]. Accident Analysis & Prevention, 2019(127): 87-95. http://www.onacademic.com/detail/journal_1000041580367699_c5b4.html
[6]	XU Chengcheng, BAO Jie, LIU Pan, et al. Investigation of contributing factors to extremely severe traffic crashes using survival theory[J]. International Journal of Injury Control and Safety Promotion, 2018, 25(2): 141-153. doi: 10.1080/17457300.2017.1363784
[7]	由冰玉, 廉福绵, 孟祥海. 基于故障树贝叶斯网的山区高速公路事故成因分析[J]. 交通信息与安全, 2019, 37(4): 44-51. doi: 10.3963/j.issn.1674-4861.2019.04.006 YOU Bingyu, LIAN Fumian, MENG Xianghai. Cause analysis of mountain expressway accidents based on fault tree Bayesian networks[J]. Journal of Transport Information and Safety, 2019, 37(4): 44-51. (in Chinese) doi: 10.3963/j.issn.1674-4861.2019.04.006
[8]	柳昕汝. 山区高速公路伤亡事故故障树及贝叶斯网络模型[D]. 哈尔滨: 哈尔滨工业大学, 2019. LIU Xinru. Fault tree and Bayesian network model of casualty accidents on mountain expressway[D]. Harbin: Harbin Institute of Technology, 2019. (in Chinese)
[9]	杜秀丽. 重特大道路交通事故致因研究[D]. 上海: 华东师范大学, 2016. DU Xiuli. Research on the causes of major road traffic accidents[D]. Shanghai: East China Normal University, 2016. (in Chinese)
[10]	刘勇, 罗德林, 石翠, 等. 基于T-S模糊故障树的多态导航系统性能可靠性[J]. 北京航空航天大学学报, 2021, 47(2): 240-246. https://www.cnki.com.cn/Article/CJFDTOTAL-BJHK202102007.htm LIU Yong, LUO Delin, SHI Cui, et al. Performance reliability of multi-state navigation system based on T-S fuzzy fault tree[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47(2): 240-246. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-BJHK202102007.htm
[11]	罗彦斌, 陈建勋, 王梦恕. 基于T-S模糊故障树理论的公路隧道冻害分析方法[J]. 北京交通大学学报, 2012, 36(4): 55-60. doi: 10.3969/j.issn.1673-0291.2012.04.011 LUO Yanbin, CHEN Jianxun, WANG Mengshu. Highway tunnel freezing damage analysis method based on T-S fuzzy fault tree theory[J]. Journal of Beijing Jiaotong University, 2012, 36(4): 55-60. (in Chinese) doi: 10.3969/j.issn.1673-0291.2012.04.011
[12]	付大伟. 基于贝叶斯网络汽车起重机液压系统可靠性分析[D]. 太原: 太原科技大学, 2016. FU Dawei. Reliability analysis of hydraulic system of truck crane based on Bayesian network[D]. Taiyuan: Taiyuan University of Science and Technology, 2016. (in Chinese)
[13]	KHAKZAD N, KHAN F, AMYOTTE P. Safety analysis in process facilites: Comparison of fault tree and Bayesian network approaches[J]. Reliability Engineering and System Safety, 2011, 96(8): 925-932. doi: 10.1016/j.ress.2011.03.012
[14]	WILSON A G, HUZURBAZAR A V. Bayesian networks for multilevel system reliability[J]. Reliability Engineering and System Safety, 2007, 92(10): 1413-1420. doi: 10.1016/j.ress.2006.09.003
[15]	周忠宝, 马超群, 周经伦. 贝叶斯网络在多态系统可靠性分析中的应用[J]. 哈尔滨工业大学学报, 2009, 41(6): 232-235. doi: 10.3321/j.issn:0367-6234.2009.06.054 ZHOU Zhongbao, MA Chaoqun, ZHOU Jinglun. The application of Bayesian networks in the reliability analysis of polymorphic systems[J]. Journal of Harbin Institute of Technology, 2009, 41(6): 232-235. (in Chinese) doi: 10.3321/j.issn:0367-6234.2009.06.054
[16]	张荧驿. 基于T-S重要度和贝叶斯网络的多态液压系统可靠性分析[D]. 秦皇岛: 燕山大学, 2011. ZHANG Yingyi. Reliability analysis of multi-state hydraulic system based on T-S importance and Bayesian network[D]. Qinhuangdao: Yanshan University, 2011. (in Chinese)
[17]	陈东宁, 姚成玉, 党振. 基于T-S模糊故障树和贝叶斯网络的多态液压系统可靠性分析[J]. 中国机械工程, 2013, 24(7): 899-905. doi: 10.3969/j.issn.1004-132X.2013.07.011 CHEN Dongning, YAO Chengyu, DANG Zhen. Reliability analysis of multi-state hydraulic system based on T-S fuzzy fault tree and Bayesian network[J]. China Mechanical Engineering, 2013, 24(7): 899-905. (in Chinese) doi: 10.3969/j.issn.1004-132X.2013.07.011
[18]	陈舞, 王浩, 张国华, 等. 基于T-S模糊故障树和贝叶斯网络的隧道坍塌易发性评价[J]. 上海交通大学学报, 2020, 54(8): 820-830. https://www.cnki.com.cn/Article/CJFDTOTAL-SHJT202008009.htm CHEN Wu, WANG Hao, ZHANG Guohua, et al. Evaluation of tunnel collapse susceptibility based on T-S fuzzy fault tree and Bayesian network[J]. Journal of Shanghai Jiaotong University, 2020, 54(8): 820-830. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-SHJT202008009.htm
[19]	SUN Honghua, CHEN Hongxia, LI Chunwei. Reliability modeling and analysis of complex multi-state systems based on weighted triangular fuzzy numbers T-S fault tree[J]. International Journal of Performability Engineering, 2019, 15(6): 1662-1671. doi: 10.23940/ijpe.19.06.p17.16621671
[20]	《中国公路》编辑部. 我国道路交通事故主要成因和特点分析[J]. 中国公路, 2018(6): 26-27. https://www.cnki.com.cn/Article/CJFDTOTAL-GLZG201806017.htm China Highway Editorial Department. Analysis of the main causes and characteristics of road traffic accidents in my country[J]. China Highway, 2018(6): 26-27. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-GLZG201806017.htm

Relative Articles

Supplements(0)

Cited By

Proportional views

Proportional views

通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

Figures(5) / Tables(4)

Get Citation

PDF

XML

Article Metrics

Article views (1449) PDF downloads(205)

A Cause Analysis of Extraordinarily Severe Traffic Crashes Based on T-S Fuzzy Fault Tree and Bayesian Network

doi: 10.3963/j.jssn.1674-4861.2021.04.006

Abstract

References

Proportional views

Catalog

通讯作者: 陈斌, bchen63@163.com

Article Metrics

Proportional views

Related

A Cause Analysis of Extraordinarily Severe Traffic Crashes Based on T-S Fuzzy Fault Tree and Bayesian Network

doi: 10.3963/j.jssn.1674-4861.2021.04.006

Abstract

References

Proportional views

Catalog

通讯作者: 陈斌, bchen63@163.com

Article Metrics

Proportional views

Related

Export File

Citation

Format

Content