基于部分优势比的高速公路路产损失严重度分析

doi:10.3976/j.issn.1002-4026.2022.05.010

Abstract

Abstract:

Based on the data of 814 accidents that caused road property losses on the southwest mountain expressway, to clarify the correlation factors of the road-property-loss severity, the road property losses caused by accidents are divided into three levels according to the amount of compensation: <4 000, 4 000~10 000, and >10 000 RMB. Further, potential factors affecting the severity of road property losses are selected from the aspects of vehicle type, number of vehicles involved, accident form and cause, and driving environment. An analysis model of the road-property-loss severity on mountain highways is established using partial advantage ratio model, and the degree of influence on road property loss due to various factors is analyzed via a pseudoelastic coefficient. The results show that 13 factors are considerably related to the degree of road property loss. Among these factors, the proportional advantage assumption is not met when the vehicles involved in accidents are small and medium-sized trucks, buses, and cars and the season is in winter, and the remaining factors meet the proportional advantage assumption. When the vehicle involved in an accident is a heavy truck, it increases the probability of a Level 2 and Level 3 road-property-loss accident; the same happens at the sections of the road with a small curve radius. In addition, smooth curve section, multivehicle accidents, weather, and straight sections will cause an increase in the probability of Level 2 and Level 3 road-property-loss accidents.

Key words: transportation engineering, degree of road production loss, partial odds ratio model, proportional advantage assumption, elastic analysis

CLC Number:

U491.3

YAO Hong-yun, CAO Zhi-fu, TU Qiang. Severity analysis of highway production loss based on partial odds ratio[J].Shandong Science, 2022, 35(5): 80-88.

Figures/Tables 7

Fig.1

Fig.2

Table 1

Table 2

Classification and coding of explanatory variables"

变量名称	编码	统计量	占比
路产损失程度 $y i$	1表示1级路产损失事故	449	55.16%
	2表示2级路产损失事故	252	30.96%
	3表示3级路产损失事故	113	13.88%
曲线半径 $x 1$	1表示小于1 000 m	219	26.90%
	2表示1 000 m≤曲线半径≤2 000 m	106	13.02%
	3表示曲线半径大于2 000 m	489	60.07%
缓和曲线 $x 2$	0表示无缓和曲线	529	64.99%
缓和曲线 $x 2$	1表示有缓和曲线	285	35.01%
肇事车辆类型 $x 3$	1表示重大货车	173	21.25%
	2表示中小货车	174	21.38%
	3表示客车	150	18.43%
	4表示轿车	317	38.94%
涉事车辆数 $x 4$	0表示单车事故	652	80.10%
涉事车辆数 $x 4$	1表示多车事故	162	19.90%
涉事车辆是否含有货车 $x 5$	0表示不包含	434	53.32%
涉事车辆是否含有货车 $x 5$	1表示包含	380	46.68%
事故形态 $x 6$	1表示追尾事故	77	9.46%
	2表示擦挂事故	64	7.86%
	3表示撞固定物事故	563	69.16%
	4表示翻车事故	87	10.69%
	5表示其他事故形态	21	2.58%
事故原因 $x 7$	1表示行车间距不足	62	7.62%
	2表示驾驶员操作不当	708	86.98%
	3表示不按规定变道或违法驾驶等	36	4.42%
	4表示其他事故原因	7	0.86%
事故发生位置 $x 8$	1表示平直路段	775	95.21%
事故发生位置 $x 8$	0表示隧道、收费站等特殊路段	39	4.79%
事故发生时间 $x 9$	1表示0:01—7:00	176	21.62%
	2表示7:01—19:00	535	65.72%
	3表示19:01—24:00	103	12.65%
行驶方向 $x 10$	0表示下行	415	50.98%
行驶方向 $x 10$	1表示上行	399	49.02%
天气情况 $x 11$	0表示非雨雾天气	604	74.20%
天气情况 $x 11$	1表示雨雾天气	210	25.80%
季节 $x 12$	1表示春天	211	25.92%
	2表示夏天	205	25.18%
	3表示秋天	204	25.06%
	4表示冬天	194	23.83%

Table 2

Table 3

Dummy variable for the types of vehicle involved"

肇事车辆类型	虚拟变量
肇事车辆类型	$x 2 1$	$x 3 1$	$x 4 1$
重大货车	0	0	0
中小货车	1	0	0
客车	0	1	0
轿车	0	0	1

Table 3

Table 4

Parameter estimation of partial proportional odds model"

变量名称	编码	1级和2级损失事故之间的界限			2级和3级损失事故之间的界限
变量名称	编码	参数估计	标准误差	P值	参数估计	标准误差	P值
截距1		-0.422	0.494	<0.001	-1.737	0.497	<0.001
曲线半径	$x 12$	-1.078	0.309	<0.001	-1.078	0.309	<0.001
曲线半径	$x 13$	-0.620	0.343	0.071	-0.620	0.343	0.071
缓和曲线	x₂	0.574	0.318	0.071	0.574	0.318	0.071
肇事车辆类型	$x 32$	-1.398	0.247	<0.001	-2.228	0.313	<0.001
	$x 33$	-2.119	0.269	<0.001	-2.862	0.405	<0.001
	$x 34$	-1.933	0.233	<0.001	-3.032	0.326	<0.001
涉事车辆数	x₄	0.573	0.342	0.094	0.573	0.342	0.094
涉事车辆是否含有货车	x₅	-0.629	0.378	0.096	-0.629	0.378	0.096
事故原因	$x 71$	-0.955	0.344	0.005	-0.955	0.344	0.005
事故原因	$x 73$	-1.038	0.437	0.018	-1.038	0.437	0.018
事故发生位置	x₈	1.811	0.478	<0.001	1.811	0.478	<0.001
天气	x₁₁	0.400	0.163	0.014	0.400	0.163	0.014
季节	$x 124$	-0.130	0.184	—	-0.655	0.305	0.032

Table 4

Table 5

Partial proportional odds model explanatory variables pseudoelastic analysis"

变量名称	编码	1级损失		2级损失		3级损失
变量名称	编码	发生概率增量	P值	发生概率增量	P值	发生概率增量	P值
曲线半径	$x 12$	3.14%	<0.001	-7.20%	0.006	-10.33%	0.001
曲线半径	$x 13$	7.68%	0.060	-6.61%	0.097	-14.29%	0.074
缓和曲线	x₂	-8.95%	0.084	8.48%	0.058	17.43%	0.069
肇事车辆类型	$x 32$	14.04%	<0.001	-9.33%	0.016	-43.23%	<0.001
	$x 33$	12.02%	<0.001	-22.45%	<0.001	-49.97%	<0.001
	$x 34$	26.10%	<0.001	-40.18%	<0.001	-112.82%	<0.001
涉事车辆数	x₄	-4.89%	—	4.86%	0.074	9.75%	0.091
涉事车辆是否含有货车	x₅	3.96%	0.075	-2.87%	—	-6.84%	—
事故原因	$x 71$	2.51%	<0.001	-3.94%	0.037	-6.45%	0.009
事故原因	$x 73$	1.27%	<0.001	-2.99%	0.068	-4.26%	0.024
事故发生位置	x₈	-79.96%	<0.001	67.61%	<0.001	147.57%	<0.001
天气	x₁₁	-5.59%	0.028	2.75%	<0.001	8.34%	0.011
季节	$x 124$	1.23%	—	0.63%	—	-14.23%	0.039

Table 5

References 24

[1]	中华人民共和国公安部. 道路交通事故处理程序规定[EB/OL]. [2021-12-25]. https://app.mps.gov.cn/gdnps/pc/content.jsp?id=8279758.
[2]	WANG Y G, PRATO C G. Determinants of injury severity for truck crashes on mountain expressways in China: A case-study with a partial proportional odds model[J]. Safety Science, 2019, 117: 100-107. DOI: 10.1016/j.ssci.2019.04.011. doi: 10.1016/j.ssci.2019.04.011
[3]	杨文臣, 谢碧珊, 房锐, 等. 山区双车道公路机动车碰撞事故严重度致因比较分析与预测[J]. 交通运输系统工程与信息, 2021, 21(1): 190-195. DOI: 10.16097/j.cnki.1009-6744.2021.01.029.
[4]	董傲然, 王长帅, 秦丹, 等. 机动车-行人事故中行人伤害严重程度分析[J]. 中国安全科学学报, 2020, 30(11): 141-147. DOI: 10.16265/j.cnki.issn1003-3033.2020.11.021. doi: 10.16265/j.cnki.issn 1003-3033.2020.11.021
[5]	POUR-ROUHOLAMIN M, ZHOU H G. Investigating the risk factors associated with pedestrian injury severity in Illinois[J]. Journal of Safety Research, 2016, 57: 9-17. DOI: 10.1016/j.jsr.2016.03.004. doi: 10.1016/j.jsr.2016.03.004
[6]	陶含笑. 公路路产损害赔(补)偿标准化制度研究[D]. 西安: 长安大学, 2019.
[7]	刘善亮. 公路路产保护研究[D]. 西安: 长安大学, 2013.
[8]	李敏. 公路路政管理中的路产保护研究[D]. 西安: 长安大学, 2013.
[9]	MA Z L, CHIEN S I J, DONG C J, et al. Exploring factors affecting injury severity of crashes in freeway tunnels[J]. Tunnelling and Underground Space Technology, 2016, 59: 100-104. DOI: 10.1016/j.tust.2016.06.013. doi: 10.1016/j.tust.2016.06.013
[10]	DONG C J, KHATTAK A J, SHAO C F, et al. Exploring the factors contribute to the injury severities of vulnerable roadway user involved crashes[J]. International Journal of Injury Control and Safety Promotion, 2019, 26(3): 302-314. DOI: 10.1080/17457300.2019.1595665. doi: 10.1080/17457300.2019.1595665 pmid: 31169068
[11]	刘辉, 章国鹏, 王羿童, 等. 无控制交叉口交通安全影响因素分析[J]. 公路交通科技, 2019, 15(9): 110-116. DOI: 10.3969/j.issn.1002-0268.2019.09.015. doi: 10.3969/j.issn.1002-0268.2019.09.015
[12]	林庆丰, 邓院昌. 基于Logistic的城市公交事故严重程度影响因素分析:以广东省为例[J]. 中山大学学报(自然科学版), 2020, 59(4): 120-127. DOI: 10.13471/j.cnki.acta.snus.2019.04.10.2019b035. doi: 10.13471/j.cnki.acta.snus.2019.04.10.2019b035
[13]	张兵, 黄娜娜, 徐伟硕, 等. 基于旅客出行特征的运输通道客运分担率预测模型:以昌吉赣通道为例[J]. 公路交通科技, 2020, 37(5): 149-158. DOI: 10.3969/j.issn.1002-0268.2020.05.018. doi: 10.3969/j.issn.1002-0268.2020.05.018
[14]	李军, 贾顺平. 道路客运提前购票行为模型[J]. 公路交通科技, 2019, 36(10): 144-151. DOI: 10.3969/j.issn.1002-0268.2019.10.019. doi: 10.3969/j.issn.1002-0268.2019.10.019
[15]	KAPLAN S, PRATO C G. Risk factors associated with bus accident severity in the United States: A generalized ordered logit model[J]. Journal of Safety Research, 2012, 43(3): 171-180. DOI: 10.1016/j.jsr.2012.05.003. doi: 10.1016/j.jsr.2012.05.003 pmid: 22974682
[16]	SAVOLAINEN P T, MANNERING F L, LORD D, et al. The statistical analysis of highway crash-injury severities: A review and assessment of methodological alternatives[J]. Accident Analysis & Prevention, 2011, 43(5): 1666-1676. DOI: 10.1016/j.aap.2011.03.025. doi: 10.1016/j.aap.2011.03.025
[17]	FU V K. Estimating generalized ordered logit models[J]. Stata Technical Bulletin, 1998, 8(44):1-44.
[18]	柳本民, 闫寒. 基于SVM事故分类的连环追尾事故影响因素分析[J]. 交通信息与安全, 2020, 38(1): 43-51. DOI: 10.3963/j.jssn.6174-4861.2020.01.006. doi: 10.3963/j.jssn.6174-4861.2020.01.006
[19]	李英帅, 张旭, 王卫杰, 等. 基于随机森林的电动自行车骑行者事故伤害程度影响因素分析[J]. 交通运输系统工程与信息, 2021, 21(1): 196-200. DOI: 10.16097/j.cnki.1009-6744.2021.01.030.
[20]	JAMAL A, UMER W. Exploring the injury severity risk factors in fatal crashes with neural network[J]. International Journal of Environmental Research and Public Health, 2020, 17(20): 7466. DOI: 10.3390/ijerph17207466. doi: 10.3390/ijerph17207466
[21]	景鹏, 黄芳, 徐刚, 等. 自动驾驶支付意愿及影响因素分析[J]. 长安大学学报(自然科学版), 2021, 41(1): 90-102. DOI: 10.19721/j.cnki.1671-8879.2021.01.010. doi: 10.19721/j.cnki.1671-8879.2021.01.010
[22]	MANNERING F, BHAT C R, SHANKAR V, et al. Big data, traditional data and the tradeoffs between prediction and causality in highway-safety analysis[J]. Analytic Methods in Accident Research, 2020, 25: 100113. DOI: 10.1016/j.amar.2020.100113. doi: 10.1016/j.amar.2020.100113
[23]	WILLIAMS R. Generalized ordered logit/partial proportional odds models for ordinal dependent variables[J]. The Stata Journal: Promoting Communications on Statistics and Stata, 2006, 6(1): 58-82. DOI: 10.1177/1536867x0600600104. doi: 10.1177/1536867x0600600104
[24]	ISLAM S, JONES S L, DYE D. Comprehensive analysis of single- and multi-vehicle large truck at-fault crashes on rural and urban roadways in Alabama[J]. Accident Analysis & Prevention, 2014, 67: 148-158. DOI: 10.1016/j.aap.2014.02.014. doi: 10.1016/j.aap.2014.02.014

Severity analysis of highway production loss based on partial odds ratio

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路产损失事故等级	1级路产损失		2级路产损失		3级路产损失
路产损失事故等级	事故数	占比	事故数	占比	事故数	占比
雨雾天气	94	20.94%	80	31.75%	36	31.86%
非雨雾天气	355	79.06%	172	68.25%	77	68.14%