基于Cox比例风险模型的交通违法间隔时间研究

doi:10.16265/j.cnki.issn1003-3033.2023.03.0176

摘要/Abstract

摘要：

为探寻驾驶员因素和时间因素对城市道路机动车交通违法间隔时间的影响,收集机动车驾驶员交通违法的间隔时间和驾驶员年龄、累计积分等数据,清洗后得到包含10个影响因素的基础数据;基于生存分析算法,采用Cox比例风险回归模型,从各变量对再次发生交通违法间隔时间的影响方面,研究驾驶员交通违法的间隔时间差异,以及影响驾驶员发生再次违法的关键因素。结果表明:交通违法时间间隔受年龄、累计积分、月份3个因素显著影响;33~44岁的驾驶者再次违法率最大,大于60岁的驾驶者再次违法率最小;驾驶员累计积分与生存曲线陡峭程度为正相关关系,累计积分与驾驶者再次违法率为正相关关系;相同间隔时间下,11月生存率最高,1月生存率最短。

关键词: Cox比例风险模型, 交通违法间隔时间, 生存分析, 影响因素, 生存率

Abstract:

In order to explore the influence of driver and time factors on the interval time of motor vehicle traffic violations on urban roads, data on the interval time, driver age, and accumulated points of motor vehicle drivers were collected. A set of 10 influencing factors was obtained after data cleaning. A Cox proportional hazard regression model based on the survival analysis algorithm was utilized to study the differences in the interval of the driver traffic offense and the key factors affecting the reccurrence of driver offense. The impact of each variable on the interval between the reccurrence of traffic offenses was analyzed. The findings indicate that the time interval of traffic violations is significantly affected by age, accumulated points, and month. Drivers aged 33 to 44 have the highest re-offending rate, while drivers over 60 have the lowest. Additionally, the cumulative points of the driver were found to be positively correlated with the steepness of the survival curve, and the accumulated points were positively correlated with the re-offending rate of the driver. Finally, the survival rates were highest in November and shortest in January at the same interval.

Key words: Cox proportional hazard model, traffic violation interval, survival analysis, influencing factor, survival rate

李聪颖, 张浩星, 谭倩, 李微, 成华, 王琦. 基于Cox比例风险模型的交通违法间隔时间研究[J]. 中国安全科学学报, 2023, 33(3): 126-133.

LI Congying, ZHANG Haoxing, TAN Qian, LI Wei, CHENG Hua, WANG Qi. Research on traffic violations interval time based on Cox proportional hazard regression model[J]. China Safety Science Journal, 2023, 33(3): 126-133.

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类别	变量	数据类型	变量解释	变量描述
驾驶员	性别	分类变量	驾驶员的性别	1:男;2:女
	年龄/岁	分类变量	驾驶员发生交通违法行为时的年龄	1:≤24;2:25~33;3:34~44; 4:45~59;5:≥60
	驾龄/a	分类变量	驾驶员发生交通违法行为距离初次领证的时间间隔	1:0;2:1~2;3:3~5;4:6~10; 5:11~15;6:>16
	违法次数	分类变量	驾驶员发生交通违法行为的次数	1:0;2:1;3:2~4;3:>4
	驾驶员是否为机动车所有人	分类变量	发生交通违法行为时的车辆归属	1:是;2:否
	累计积分	分类变量	驾驶员每次违法行为发生时所扣积分的累加	1:0;2:1~3;3:4~6;4:>6
时间	月份	数值变量	违法事件发生时的月份	变量自身数值
	时刻	数值变量	违法事件发生时的小时时刻	变量自身数值
	清分距离	数值变量	违法行为距离下一次驾驶证清分的时间间隔	变量自身数值
间隔时间	间隔时间	数值变量	驾驶员发生再次违法之间的时间差值	变量自身数值

统计量	间隔时间
Mann-Whitney U	11 468 404.500
Wilcoxon W	55 212 385.500
Z	-1.136
显著性	0.256
变量分组:性别

变量	统计量	间隔时间	累计积分	年龄	驾龄
间隔时间	Spearman相关	1.000	-0.166^**	0.027	0.013
间隔时间	显著性	—	0.000	0.003	0.165

变量	离差平方和	自由度	均方差	F值	显著性
驾驶员是否为机动车所有人	4 6613.368	1.000	46 613.368	6.135	0.013
	8 996 5441.645	11 840	7 598.433	—	—
	90 012 055.012	11 841	—	—	—

变量	统计量	间隔时间	月份	时刻
间隔时间	Pearson相关	1.000	0.445^**	0.005
间隔时间	显著性	—	0.000	0.573