Grey correlation analysis of railway accidents

doi:10.16265/j.cnki.issn1003-3033.2022.S2.0076

Abstract

Abstract:

This study aimed to make clear grey correlations between railway accidents and responsible departments. The direct causes of safety accidents were analyzed with statistical data in China railway yearbooks from 2014 to 2018. According to the main responsible departments involved, accidents were divided into five categories: vehicle accident, electrical accident, engineering accident, maintenance accident and off-road accident. Their key causes were found by investigating the duties of main responsible departments through grey correlation analysis with SPSS. The results show that crossing lines and protective facilities by people is the main inducement of off-road collision accidents. Thus, it takes priority to strengthen the safety and law-abiding awareness of residents along lines. The grey correlation analysis results of factors influencing railway accidents indicate that off-road accidents have the highest correlation degree, followed by vehicle accidents, maintenance accidents, engineering accidents and electrical accidents.

Key words: railway accidents, grey correlation analysis, responsible departments, off-road accident, influencing factors

ZHANG Boxu. Grey correlation analysis of railway accidents[J]. China Safety Science Journal, 2022, 32(S2): 60-63.

Figures/Tables 4

References 16

[1]	花玲玲, 郑伟. 基于复杂网络理论的铁路事故致因分析[J]. 中国安全科学学报, 2019, 29(增1):114-119.
	HUA Lingling, ZHENG Wei. Research on causation of railway accidents based on complex network theory[J]. China Safety Science Journal, 2019, 29(S1):114-119. doi: 10.16265/j.cnki.issn1003-3033.2019.S1.021
[2]	王博, 李子华, 李晓宇. 基于事故统计的世界铁路旅客运输安全分析研究[J]. 铁道技术监督, 2016, 44(10):34-38.
[3]	王海震, 陈奕潇, 颜景晨, 等. 减少铁路路外伤亡事故的方法分析[J]. 运输经理世界, 2020(13):137-138.
[4]	何景师, 戴航, 刘小慧. 基于熵权-AHP混合约束DEA法的铁路车务行车风险评价研究[J]. 铁道运输与经济, 2020, 42(9):99-105.
	HE Jingshi. DAI Hang, LIU Xiaohui. A study on the risk evaluation of railway fleet operation based on the dea method of entropy weight and ahp mixed constraint[J]. Railway Transport and Economy, 2020, 42(9):99-105.
[5]	TALEBNEJAD A, NADAF A. The use of grey system theory in predicting the road traffic accident in fars province in iran[J]. Australian Journal of Business and Management Research, 2011, 1(9):18-23.
[6]	马佳骥. 铁路行车安全分析与探讨[J]. 交通企业管理, 2021, 36(2):96-97.
	MA Jiaji. Analysis and discussion on railway safety[J]. Transportation Enterprise Management, 2021, 36(2):96-97.
[7]	韩广, 卜桐, 王明明, 等. 基于双通道双向长短时记忆网络的铁路行车事故文本分类[J]. 铁道学报, 2021, 43(9):71-79.
	HAN Guang, BU Tong, WANG Mingming, et al. Text classification of railway traffic accidents based on dual-channel bidirectional long short term memory network[J]. journal of the China Railway Society, 2021, 43(9):71-79.
[8]	左博睿, 帅斌, 黄文成. 基于ISM-24Model的重大铁路事故组织致因研究[J]. 中国安全科学学报, 2020, 30(10):47-54. doi: 10.16265/j.cnki.issn1003-3033.2020.10.007
	ZUO Borui, SHUAI Bin, HUANG Wencheng. Research on deep organizational cause analysis of serious railway accidents based on ISM-24Model[J]. China Safety Science Journal, 2020, 30(10):47-54. doi: 10.16265/j.cnki.issn1003-3033.2020.10.007
[9]	中国铁路总公司. 中国铁道年鉴2014[M]. 北京: 中国铁道出版社, 2015:45-66.
[10]	中国铁路总公司. 中国铁道年鉴2015[M]. 北京: 中国铁道出版社, 2016:98-105.
[11]	中国铁路总公司. 中国铁道年鉴2016[M]. 北京: 中国铁道出版社, 2017:154-158.
[12]	中国铁路总公司. 中国铁道年鉴2017[M]. 北京: 中国铁道出版社, 2018:232-238.
[13]	中国国家铁路集团有限公司. 中国铁道年鉴2018[M]. 北京: 中国铁道出版社, 2018:301-306.
[14]	李建国, 向万里, 王久梗. 基于灰色遗传BP神经网络的铁路货运量预测[J]. 科技和产业, 2022, 22(1):119-124.
	LI Jianguo, XIANG Wanli, WANG Jiugeng. Forecasting of railway freight volume based on grey genetic bp neural network[J]. Science Technology and Industry, 2022, 22(1):119-124.
[15]	李飞. 基于灰色关联度的铁路事故预测研究[D]. 兰州.兰州交通大学, 2017.
	LI fei. Research on railway accident data forecasting method based on the correlation analysis[D]. Lanzhou.Lanzhou Jiaotong University, 2017.
[16]	胡哨刚, 孔祥芳, 旷利平, 等. 基于灰色理论的铁路交通一般事故数据预测模型[J]. 交通科技与经济, 2021, 23(4):27-31.
	HU Shaogang, KONG Xiangfang, KUANG Liping, et al. Data forecast model of general railway traffic accidents based on grey theory[J]. Technology & Economy in Areas of Communications, 2021, 23(4):27-31.

年份	X₀	X₁	X₂	X₃	X₄	X₅
2014	0.90	-0.85	0.11	0.13	-0.20	1.55
2015	0.39	-0.95	1.59	0.71	-0.53	0.44
2016	0.84	0.35	-0.01	1.20	1.5	-0.43
2017	-0.94	-0.05	-0.10	-1.01	0.34	-0.72
2018	-1.20	1.50	-0.73	-1.01	-1.13	-0.85

年份	X₀	X₁	X₂	X₃	X₄	X₅
2014	0.665	0.611	0.621	0.649	1.000	0.665
2015	0.685	0.636	0.647	0.663	0.933	0.685
2016	0.687	0.613	0.635	0.687	0.827	0.687
2017	0.769	0.679	0.683	0.746	0.933	0.769
2018	0.820	0.692	0.694	0.722	0.942	0.820