基于事故修正系数的山区高速公路事故预测模型

doi:10.16265/j.cnki.issn1003-3033.2023.01.0472

摘要/Abstract

摘要：

为量化分析山区高速公路线形指标对交通事故发生及严重程度的影响,引入事故修正系数(CMF)概念,提出基于CMF的高速公路交通事故预测模型。验证事故数据服从零膨胀负二项(ZINB)分布,并标定10个不同线形组合路段的基本事故预测模型;在界定理想条件的基础上,借助优势比分析方法,建立10个不同线形组合相对于基本预测模型的CMF模型;通过弹性分析识别出对不同严重程度事故有显著影响的因素,并以此分析山区高速公路线形指标及线形组合的安全效应。研究结果表明:CMF能够定量反映不同线形组合的风险效应,且其值越高,事故率及事故严重程度可能更高;山区高速公路中,平曲线缓坡组合相对安全,平曲线陡坡组合、平曲线竖曲线组合均有较高的事故风险;平曲线与凹型竖曲线组合时的风险会略低于其与凸形竖曲线组合;控制纵坡度、减少平竖曲线的组合均有利于山区高速公路交通安全。

关键词: 事故修正系数(CMF), 山区高速公路, 事故预测模型, 严重程度, 零膨胀负二项(ZINB)

Abstract:

In order to quantitatively analyze the influence of mountain highway alignment index on the occurrence and severity of traffic accidents, the concept of CMF was introduced, and a highway traffic accident prediction model based on CMF was proposed. The accident data were verified to obey the zero-inflated negative binomial distribution, and ten basic accident prediction models for different alignment combinations of road sections were determined. With the help of the dominance ratio analysis method, ten crash modification factor models of different alignment combinations with respect to the basic prediction model were established. The factors having significant influence on accidents of different severity were identified by elasticity analysis, and the safety effects of highway alignment index and combination of alignment were analyzed. The results show that: CMF can quantitatively reflect the risk effect of different alignment combinations. The higher the value, the higher the accident rate and accident severity may be. In the mountain highway, the combination of horizontal curve and gentle slope is relatively safe, while the combination of horizontal curve and steep slope and the combination of horizontal curve and vertical curve have higher accident risk. The risk of combination of horizontal curve and concave vertical curve is slightly lower than that of combination of horizontal curve and convex vertical curve. Controlling vertical slope and reducing the combination of horizontal curve and vertical curve are beneficial to highway safety in mountainous areas.

Key words: crash modification factors (CMF), mountainous freeway, accident prediction model, accident severity, zero inflated negative binomial (ZINB)

孟祥海, 由冰玉, 邱志雄, 李之啸, 张明扬. 基于事故修正系数的山区高速公路事故预测模型[J]. 中国安全科学学报, 2023, 33(1): 10-17.

MENG Xianghai, YOU Bingyu, QIU Zhixiong, LI Zhixiao, ZHANG Mingyang. Accident prediction model of mountainous freeway based on crash modification factors[J]. China Safety Science Journal, 2023, 33(1): 10-17.

图/表 10

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表2

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表4

表5

图1

图2

表6

各路段最危险、安全线形条件

路段类型	最危险线形条件	最安全线形条件
C_G (G≤1%)	CR-500 m,CL- 800 m,G-0.5%	CR-5 000 m,CL-800 m, G-0.11%
C_G (G>1%)	CR-500 m,CL- 600 m,G-4.59%	CR-5 011 m,CL-800 m, G-1.21%
S_CV、 $S S V$	基准条件	SVR-5 000 m,G-5.09%
C_SV	CR-500 m,CL- 600 m,GD-5.45%	CR-4 978 m,CL-800 m, GD-0.2%
$C C V$	CR-500 m,CL- 600 m,GD-6.24%	CR-8 111 m,CL-1 000 m, GD-0.32%

表6

表7

图3

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路段单元名称	符号	个数
直线+纵坡	S_G	194
直线+凹型竖曲线	S_SV	140
平曲线+凸型竖曲线	C_CV	104
直线+凸型竖曲线	S_CV	109
平曲线+纵坡	C_G	207
平曲线+凹型竖曲线	C_SV	151

变量类型	变量名称及单位	平均值	标准差	最小值/最大值
因变量	路段单元年事故数N	0.738	1.79	0/17
自变量	年平均日交通量(Annual Average Daily Traffic,AADT)/(Veh·d^-1)	11 344	12.43	8 675/17 643
	大型车比例(Proportion of Large Vehicles,PHV)	0.52	0.12	0.23/0.75
	平曲线半径(Curve Radius,CR)/km	0.825	20.32	0/8.11
	平曲线偏角(Deflection Angles,DA)/rad	0.48	2.52	0/5.63
	平曲线长度(Curve Length,CL)/km	0.388	0.95	0/0.18
	纵坡坡度G/%	-0.22	1.91	-5.036/5.092
	纵坡坡度差(Gradient Difference,GD)/%	-0.002 3	1.29	-5.61/5.092
	凸型竖曲线半径(Convex Vertical Radius,CVR)/km	4.824	66.456	0/200
	凹型竖曲线半径(Sunken Vertical Radius,SVR)/km	5.919	88.065	0/300
	竖曲线长度(Vertical Curve Length,VCL)/km	0.101	0.44	0/0.941

变量名称	参数值	标准误	z值	p值	95%置信区间
ln(AADT)	1.09	0.03	3.144	0.000	1.04	1.15
G/%	0.073	0.007	5.7	0.000	0.066	0.089
ln(1 800/CR)	0.561	0.002	11.34	0.000	0.502	0.63
1/CR×1/CL	1.293	0.056	4.463	0.000	0.896	1.504
截距项_cons	-0.939	0.009	7.7	0.001	-1.048	-0.894
过离散参数α	1.86	0.13	—	—	1.708	1.976

线形	基本事故预测模型
C_G (G≤1%)	N_FI=exp[1.09ln(AADT)+0.073G+0.561ln(1 800/CR)+1.293(1/CR)(1/CL)-6.939]
C_G (G≤1%)	N_PDO=exp[1.67ln(AADT)+0.071G+0.421ln(1 800/CR)+1.208(1/CR)(1/CL)-3.299]
C_G (G>1%)	N_FI=exp[1.01ln(AADT)+0.07G²+0.413ln(900/CR)+1.127(1/CR)(1/CL)+0.67]
C_G (G>1%)	N_PDO=exp[1.08ln(AADT)+0.047G²+0.39ln(900/CR)+1.084(1/CR)(1/CL)+0.556]
S_CV、S_SV	N_FI=exp[1.53ln(AADT)+0.052G²+2.317] N_PDO=exp[1.795ln(AADT)+0.044G²+1.873]
C_SV	N_FI=exp[1.05ln(AADT)+298.301(A/CR)+22.091(A/CL)+0.327ln(1 000/CR)+8.702(1/CR)(1/CL)-3.28]
C_SV	N_PDO=exp[1.327ln(AADT)+277.903(A/CR)+20.023(A/CL)+0.307ln(1 000/CR)+8.204(1/CR)(1/CL)-2.936]
C_CV	N_FI=exp[1.13ln(AADT)+380.14(A/CR)+0.263ln(2 000/CR)+14.901(1/CR)(1/CL)-1.462]
C_CV	N_PDO=exp[1.302ln(AADT)+346.992(A/CR)+0.259ln(2 000/CR)+12.88(1/CR)(1/CL)-1.202]

线形	CMF模型
C_G (G≤1%)	CMF_FI=exp[0.073G+0.561ln(1 800/CR)+1.293(1/CR)(1/CL)]
C_G (G≤1%)	CMF_PDO=exp[0.071G+0.42ln(1 800/CR)+1.208(1/CR)(1/CL)]
C_G (G>1%)	CMF_FI=exp[0.07G²+0.413ln(900/CR)+1.127(1/CR)(1/CL)]
C_G (G>1%)	CMF_PDO=exp[0.047G²+0.39ln(900/CR)+1.084(1/CR)(1/CL)]
S_CV、S_SV	CMF_FI=exp[1.53ln(AADT)+0.052G²] CMF_PDO=exp(0.044G²)
C_SV	CMF_FI=exp[298.301(A/CR)+22.091(A/CL)+0.327ln(1 000/CR)+8.702(1/CR)(1/CL)]
C_SV	CMF_PDO=exp[277.903(A/CR)+20.023(A/CL)+0.307ln(1 000/CR)+8.204(1/CR)(1/CL)]
C_CV	CMF_FI=exp[380.14(A/CR)+0.263ln(2 000/CR)+14.901(1/CR)(1/CL)]
C_CV	CMF_PDO=exp[346.992(A/CR)+0.259ln(2 000/CR)+12.88(1/CR)(1/CL)]