Research on risk factors of hazardous material highway transport accidents based on association rules

doi:10.16265/j.cnki.issn1003-3033.2023.06.1618

Abstract

Abstract:

In order to understand the characteristics and causes of hazmat transport accidents, detailed data of 1 721 hazmat transport accidents was collected. After eliminating invalid and lost accident records, 6 direct risk factors and their 18 subfactors were extracted. Furthermore, the attribute value of each risk factor variable was coded. The association rule mining technology based on the Apriori algorithm was used to quantify the degree of correlation between risk factors. After inductive analysis, 35 valuable association rules were found to explore the influence of different risk factor combinations on hazmat transport accidents. The results show that 24% of hazmat transport accidents occur on trucks carrying 24-39 tons of flammable liquid, and 13% of hazmat transport accidents involve corrosive substances, and the consequence is leakage. As for accident types, 34% of hazmat transportation accidents are rollover accidents of a single truck, and the lower the road grade is, the greater the possibility of a rollover accident. Hazmat transport accidents on low-grade highways are more likely to occur at night, because of the complex horizontal, vertical lines and imperfect traffic safety facilities of low-grade roads and the driver's reaction ability and vision conditions are constrained.

Key words: association rule, hazardous material, highway transport accidents, accident risk factor

WANG Aidi, XING Yingying, ZHANG Shiwen, LU Jian. Research on risk factors of hazardous material highway transport accidents based on association rules[J]. China Safety Science Journal, 2023, 33(6): 159-165.

Figures/Tables 3

Tab.1

Tab.2

References 20

[1]	吴宗之, 孙猛. 200起危险化学品公路运输事故的统计分析及对策研究[J]. 中国安全生产科学技术, 2006, 2(2):3-8.
	WU Zongzhi, SUN Meng. Statistical analysis and countermeasure research on 200 road transportation accidents of hazardous chemicals[J]. Journal of Safety Science and Technology, 2006, 2(2):3-8.
[2]	危险品运输车发展现状危险品运输车行业需求分析[EB/OL]. [2022-05-19].https://www.chinairn.com/news/20220519/115548590.shtml.
[3]	杨东. 道路危险货物运输事故规律挖掘及事故风险研究[D]. 兰州: 兰州交通大学, 2020.
	YANG Dong. Research on accident regularity and accident risk of road dangerous goods hazardous material transportation[D]. Lanzhou: Lanzhou Transportation University, 2020.
[4]	陈晓勇, 施式亮, 任竞舟, 等. 2013—2014年我国道路危险化学品运输事故统计分析及对策[J]. 湖南科技大学学报:自然科学版, 2017, 32(3): 91-95.
	CHEN Xiaoyong, SHI Shiliang, REN Jingzhou, et al. Statistical analysis and countermeasures research on the traffic accident of road dangerous chemicals from 2013-2014 in Country[J]. Hunan Science and Technology University Journal :Natural Science Edition, 2017, 32(3): 91-95.
[5]	OGGERO A, DARBRA R M, MUNOZ M, et al. A survey of accidents occurring during the transport of hazardous substances by road and rail[J]. Journal of Hazardous Materials, 2006, 133(1/2/3): 1-7. doi: 10.1016/j.jhazmat.2005.05.053
[6]	YANG Jie, LI Fengying, ZHOU Jingbo, et al. A survey on hazardous materials accidents during road transport in China from 2000 to 2008[J]. Journal of Hazardous Materials, 2010, 184(1/2/3): 647-653. doi: 10.1016/j.jhazmat.2010.08.085
[7]	张兆宁, 陈子辰, 卢飞. 配对进近侧向位置误差QAR参数关联性分析[J]. 中国安全科学学报, 2021, 31(8): 47-52. doi: 10.16265/j.cnki.issn1003-3033.2021.08.007
	ZHANG Zhaoning, CHEN Zichen, LU Fei. Correlation analysis of QAR parameters for paired approach lateral position error[J]. China Safety Science Journal, 2021, 31(8): 47-52. doi: 10.16265/j.cnki.issn1003-3033.2021.08.007
[8]	鲁义, 伍江乐, 邵淑珍, 等. 基于贝叶斯网络的危化品道路运输事故推理模型[J]. 中国安全科学学报, 2022, 32(3): 174-182. doi: 10.16265/j.cnki.issn1003-3033.2022.03.024
	LU Yi, WU Jiangle, SHAO Shuzhen, et al. Prediction model for road transport accidents of hazardous chemicals based on Bayesian network[J]. China Safety Science Journal, 2022, 32(3): 174-182. doi: 10.16265/j.cnki.issn1003-3033.2022.03.024
[9]	UDDIN M, HUYNH N. Factors influencing injury severity of crashes involving hazmat trucks[J]. International Journal of Transportation Science and Technology, 2018, 7(1): 1-9. doi: 10.1016/j.ijtst.2017.06.004
[10]	ZHU Ting, ZHAO Laijun, WANG Xulei. Analysis of the hazardous material transportation accidents based on the Bayesian network method[J]. Process Safety and Environmental Protection Safety, 2016, 16: 53-60.
[11]	MA Xiaoli, XING Yingying, LU Jian. Causation analysis of hazardous material road transportation accidents by Bayesian network using genie[J]. Journal of Advanced Transportation, 2018: DOI: 10.1155/2018/6248105. doi: 10.1155/2018/6248105
[12]	MIRABADI A, SHARIFIAN S. Application of association rules in Iranian railways (RAI) accident data analysis[J]. Safety Science, 2010, 48(10): 1427-1435. doi: 10.1016/j.ssci.2010.06.006
[13]	MONTELLA A. Identifying crash contributory factors at urban roundabouts and using association rules to explore their relationships to different crash types[J]. Accident Analysis and Prevention, 2011, 43: 1451-1463. doi: 10.1016/j.aap.2011.02.023
[14]	CHENG Chingwu, LIN Chenchung, LEU Sousen. Use of association rules to explore cause-effect relationships in occupational accidents in the Taiwan construction industry[J]. Safety Science, 2010, 48: 436-444. doi: 10.1016/j.ssci.2009.12.005
[15]	VERMA A, KHAN S D, MAITI J, et al. Identifying patterns of safety related incidents in a steel plant using association rule mining of incident investigation reports[J]. Safety Science, 2014, 70: 89-98. doi: 10.1016/j.ssci.2014.05.007
[16]	侯熙桐. 基于多维关联规则的民航事故数据挖掘研究[D]. 天津: 中国民航大学, 2016.
	HOU Xitong. Data mining of civil aviation accidents based on multi-association rules[D]. Tianjin: Civil Aviation University of China, 2016.
[17]	韩家炜美, 坎伯加, 裴健加, 等. 数据挖掘概念与技术(3版)[M].范明, 孟小峰, 译. 北京: 机械工业出版社, 2012:336-339.
	HAN Jiawei, KAMBER M, PEI Jian, et al. Data mining:concepts and techniques(3^th Edition)[M]. FAN Ming, MENG Xiaofeng, Translated. Beijing: Machinery Industry Press, 2012:336-339.
[18]	阿加沃尔美. 数据挖掘原理与实践[M]. 王晓阳, 王建勇, 译. 北京: 机械工业出版社, 2021:224-227.
	AGGARWAL C C. Data mining: the textbook[M]. WANG Xiaoyang, WANG Jianyong, Translated. Beijing: Machinery Industry Press, 2021:224-227.
[19]	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. doi: S0022-4375(18)30069-0 pmid: 30553431
[20]	詹志炜, 魏瑞, 孟庆宏, 等. G11型硫酸罐车的罐体腐蚀分析与防护[J]. 铁道运营技术, 2011, 17(3):685-689.
	ZHAN Zhiwei, WEI Rui, MENG Qinghong, et al. Analysis and protection of tank corrosion of G11 sulfuric acid tank[J]. Railway Operation Technology, 2011, 17(3): 685-689.

风险因素		属性值
危险品因素	危险品种类	爆炸品(1),气体(2),易燃液体(3),易燃固体(4),氧化性物质和有机过氧化物(5),毒性物质(6),腐蚀性物质(7),其他(8)
危险品因素	危险品运量/t	(0, 10] (1),(10, 25] (2),(24, 40] (3),(40, 100](4)
驾驶员因素	操作失误	—
	疲劳驾驶	—
	超速	—
地点因素	道路类型	城市道路(1),高速公路(2),一级公路(3),二级公路(4),三、四级公路(5)
	路面潮湿	—
	弯道	—
	坡道	—
	特殊位置(桥梁、交叉口、隧道)	—
环境因素	季节	春:3—5月(1),夏6—8月(2),秋:9—11月(3),冬:12月—次年2月(4)
	双休日	—
	时间段	黎明:5:00—7:00(1),白天:7:00—17:00:(2),黄昏:17:00—19:00(3),晚上:19:00—次日5:00(4)
	天气情况	晴天(1),阴天(2),不利天气如降雨、降雪、大雾等(3)
车辆因素	事故涉及车辆数	1辆(1),2辆(2),3辆(3),4辆以上(4)
事故因素	事故类型	追尾碰撞(1),刮擦(2),翻车或侧翻(3),迎面相撞或撞护栏(4),车辆故障如抛锚、爆胎、阀门故障、刹车失灵、自燃或起火等(5)
	事故后果	爆炸(1),起火(2),泄漏(3),无泄漏(4)
	疏散人数(事故影响范围)	不疏散(0),小范围疏散:1~100人(1),大范围疏散:100人以上(2)

序号	强关联规则	S	C	L
1	路面潮湿→天气情况3	0.16	0.90	2.81
2	事故类型1 →事故涉及车辆数2	0.19	0.81	2.44
3	危险品种类8 →季节3	0.06	0.51	2.22
4	弯道→事故类型3	0.03	0.74	2.01
5	危险品种类2 →危险品运量2	0.07	0.49	1.92
6	事故类型4 →事故涉及车辆数2	0.11	0.62	1.87
7	坡道→事故类型3	0.02	0.65	1.76
8	道路类型5 →事故类型3	0.06	0.64	1.75
9	超速→事故类型3	0.02	0.59	1.61
10	事故类型5 →事故涉及车辆数1	0.20	0.95	1.59
11	事故涉及车辆数4 →道路类型2	0.02	0.68	1.57
12	特殊位置→道路类型1	0.05	0.48	1.57
13	事故类型3 →事故涉及车辆数1	0.34	0.93	1.56
14	事故类型1 →道路类型2	0.15	0.65	1.51
15	道路类型4 →事故类型3	0.05	0.53	1.44
16	危险品种类6 →道路类型2	0.02	0.62	1.44
17	道路类型3 →危险品运量3	0.06	0.54	1.42
18	弯道→事故涉及车辆数1	0.03	0.82	1.37
19	时间段1 →事故类型1	0.03	0.31	1.34
20	超速→时间段2	0.03	0.67	1.31
21	危险品运量1 →道路类型1	0.04	0.40	1.30
22	道路类型3 →事故类型3	0.04	0.46	1.30
23	事故涉及车辆数4 →天气情况2	0.02	0.56	1.30
24	危险品种类7 →双休日	0.06	0.43	1.29
25	疏散人数1 →道路类型1	0.04	0.39	1.28
26	时间段3 →道路类型1	0.03	0.38	1.25
27	坡道1 →事故涉及车辆数1	0.02	0.74	1.23

序号	强关联规则	S	C	L
28	道路类型5 →时间段4	0.03	0.38	1.20
29	危险品运量3 →危险品种类3	0.24	0.63	1.19
30	坡道1 →事故后果3	0.03	0.96	1.18
31	事故后果2 →道路类型2	0.06	0.50	1.16
32	危险品种类7 →事故后果3	0.13	0.93	1.14
33	时间段1 →道路类型2	0.07	0.50	1.13
34	事故涉及车辆数2 →道路类型2	0.16	0.49	1.12
35	弯道→事故后果3	0.03	0.91	1.11