Research on traffic violations interval time based on Cox proportional hazard regression model

doi:10.16265/j.cnki.issn1003-3033.2023.03.0176

Abstract

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

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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References 13

[1]	郭洪洋, 韩雪松, 刘澜, 等. 驾驶员交通安全行为可靠性风险度量研究[J]. 中国安全科学学报, 2013, 23(6): 103-109.
	GUO Hongyang, HAN Xuesong, LIU Lan, et al. Study on measuring of drivers traffic safety behavior reliability risks[J]. China Safety Science Journal, 2013, 23(6): 103-109.
[2]	ABDEL-ATY M A, RADWAN A E. Modeling traffic accident occurrence and involvement[J]. Accident Analysis & Prevention, 2000, 32(5): 633-642. doi: 10.1016/S0001-4575(99)00094-9
[3]	ONA J D, MUJALLI R O, CALVO F J. Analysis of traffic accident injury severity on Spanish rural highways using Bayesian networks[J]. Accident Analysis and Prevention, 2011, 43(1):402-411. doi: 10.1016/j.aap.2010.09.010
[4]	朱彤, 秦丹, 董傲然, 等. 公交车驾驶人历史违规数据与事故责任的随机参数模型研究[J]. 安全与环境学报, 2021, 21(4): 1566-1572.
	ZHU Tong, QIN Dan, DONG Aoran, et al. Investigation on the random parameter model of bus driver history violation data and accident liability[J]. Journal of Safety and Environment, 2021, 21(4): 1566-1572.
[5]	付川云, 刘华, 王陶钰. 未按规定导向车道行驶行为干预方法研究[J]. 安全与环境学报, 2019, 19(3): 874-880.
	FU Chuanyun, LIU Hua, WANG Taoyu. Intervention methods of driving in the inaccurate oriented lane[J]. Journal of Safety and Environment, 2019, 19(3):874-880.
[6]	郭璘, 周继彪, 董升, 等. 基于改进K-means算法的城市道路交通事故分析[J]. 中国公路学报, 2018, 31(4): 270-279.
	GUO Lin, ZHOU Jibiao, DONG Sheng, et al. Analysis of urban road traffic accidents based on improved K-means algorithm[J]. China Journal of Highway and Transport, 2018, 31(4): 270-279.
[7]	BORHAN M N, IBRAHIM A N H, AZIZ A, et al. The relationship between the demographic, personal, and social factors of Malaysian motorcyclists and risk taking behavior at signalized intersections[J]. Accident Analysis & Prevention, 2018, 121:94-100. doi: 10.1016/j.aap.2018.09.004
[8]	HEQIMI G, GATES T, KAY J. Using spatial interpolation to determine impacts of annual snowfall on traffic crashes for limited access freeway segments[J]. Accident Analysis & Prevention, 2018, 121:202-212. doi: 10.1016/j.aap.2018.09.014
[9]	张光南, 钟俏婷, 杨清玄. 交通违法事故时空分布特征及其影响因素:以广州市为例[J]. 交通运输系统工程与信息, 2019, 19(3): 208-214.
	ZHANG Guangnan, ZHONG Qiaoting, YANG Qingxuan. Temporal-spatial characteristics and influencing factors of at-fault traffic crashes: a case study in Guangzhou[J]. Journal of Transportation Systems Engineering and Information Technology, 2019, 19(3):208-214.
[10]	韩天园, 田顺, 吕凯光, 等. 基于文本挖掘的重特大交通事故成因网络分析[J]. 中国安全科学学报, 2021, 31(9):150-156. doi: 10.16265/j.cnki.issn1003-3033.2021.09.021
	HAN Tianyuan, TIAN Shun, LYU Kaiguang, et al. Network analysis on causes for serious traffic accidents based on text mining[J]. China Safety Science Journal, 2021, 31(9):150-156. doi: 10.16265/j.cnki.issn1003-3033.2021.09.021
[11]	王晓勇, 罗珅, 任杰, 等. 公交驾驶员违规间隔时间及影响因素研究[J]. 中国安全科学学报, 2019, 29(6):128-133. doi: 10.16265/j.cnki.issn1003-3033.2019.06.021
	WANG Xiaoyong, LUO Shen, REN Jie, et al. Research on interval time of traffic violations for bus drivers and its influencing factors[J]. China Safety Science Journal, 2019, 29(6):128-133. doi: 10.16265/j.cnki.issn1003-3033.2019.06.021
[12]	唐新宇, 王奋, 朱海燕, 等. 基于生存分析方法的地铁车站自动扶梯通过能力研究[J]. 都市快轨交通, 2020, 33(4): 61-65.
	TANG Xinyu, WANG Fen, ZHU Haiyan, et al. Capacity of escalator in metro station based on survival analysis method[J]. Urban Rapid Rail Transit, 2020, 33(4): 61-65.
[13]	郭宏伟, 高自友, 赵小梅, 等. 路内停车对非机动车交通行为影响研究[J]. 交通运输系统工程与信息, 2011, 11(1): 79-84.
	GUO Hongwei, GAO Ziyou, ZHAO Xiaomei, et al. Traffic behavior analysis of non-motorized vehicle under influence of curb parking[J]. Journal of Transportation Systems Engineering and Information Technology, 2011, 11(1): 79-84.

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