基于随机参数logit的山区高速公路事故严重程度研究

doi:10.16265/j.cnki.issn1003-3033.2024.04.0448

摘要/Abstract

摘要：

为了探究平曲线、纵曲线、平纵组合线形对山区高速公路交通事故严重程度的影响,首先,统计分析某山区高速公路263 km路段的线形数据和该路段5年内发生的1 870起事故,并将其分类;然后,根据调研数据的结构形式,建立随机参数logit模型,进而利用该模型研究组合线形对事故严重程度的影响。结果表明:曲线比例(ROC)、加权曲率(CW)、单元坡度差(ΔS₂)等因素与仅财产损失事故增加相关,而坡长组合(CSL)与仅财产损失事故减少相关;加权曲率(CW=0.6~0.7)和连续上坡与轻伤事故的事故增加相关,加权曲率(CW=0.3~0.4)、单元坡度差(ΔS₁)、曲线比例(ROC)等因素与轻伤事故减少相关;曲率变化率(VRC)、单元半径比(CRF,CRB)、弯坡组合(CSR)等因素与重伤或死亡事故增加相关,曲线比例(ROC)与重伤或死亡事故减少相关;组合线形对事故严重程度有着显著影响。

关键词: 随机参数logit模型, 山区高速公路, 事故严重程度, 平纵组合线形, 财产损失事故

Abstract:

In order to investigate the influence of flat curve, longitudinal curve, and flat-long combination of line shapes on the severity of traffic accidents on mountain highways, firstly, the line shape data of a 263 km section of a mountain highway and the 1 870 accidents occurring on the section in five years were statistically analyzed and classified. Then, according to the structural form of the research data, the stochastic parametric logit model was established, and then the model was utilized to study the effect of the combination of line shapes on the severity of accidents. The results show that: the factors of ratio of curve (ROC), weighted curvature (CW), and delta slope (ΔS₂) are associated with an increase in property damage-only accidents, whereas the combination of slope lengths (CSL) is associated with a decrease in property damage-only accidents; the weighted curvature (CW=0.6-0.7) and continuous uphill slope are associated with an increase in accidents with minor injuries, and the factors of weighted curvature (CW=0.3-0.4), delta slope (ΔS₁), and ratio of curve (ROC) were associated with a decrease in accidents with minor injuries; the variation rate of curvature (VRC), contiguous radius of front and behind (CRF, CRB), and combination of slope and radius (CSR) were associated with an increase in accidents with serious injuries or fatalities, and the ratio of curve (ROC) was associated with a decrease in accidents with serious injuries or fatalities; and the combination of linear shapes had a significant effect on the severity of accidents.

Key words: random parameter logit model, mountain highway, severity of accident, horizontal and vertical combined alignments, property loss accident

中图分类号:

X951交通运输安全

张韡, 高晗昊, 唐鑫琦, 徐坤淼. 基于随机参数logit的山区高速公路事故严重程度研究[J]. 中国安全科学学报, 2024, 34(4): 101-110.

ZHANG Wei, GAO Hanhao, TANG Xinqi, XU Kunmiao. Research on severity of mountain highway accidents based on random parameter logit model[J]. China Safety Science Journal, 2024, 34(4): 101-110.

图/表 7

图1

表1

自变量与因变量的分类与数值

变量类别	变量名称	虚拟变量符号	变量定义	频数	占比/ %	变量类别	变量名称	虚拟变量符号	变量定义	频数	占比/ %
因变量	事故严重程度	Y₁	仅财产损失事故	1 580	86.63	纵面线形组合	ΔS₁/ %	X₆₃	1.5~2	373	20.43
		Y₂	轻伤事故	125	6.84			X₆₄	2~3	255	13.96
		Y₃	重伤或死亡事故	119	6.52			X₆₅	3~4	135	7.38
平面线形组合	ROC	X₁₁	0~0.4	177	9.68			X₆₆	>4	138	7.59
		X₁₂	0.4~0.5	160	8.77		ΔS₂/ %	参照	<0.5	117	6.42
		X₁₃	0.5~0.6	267	14.65			X₇₁	0.5~1.0	373	20.43
		参照	0.6~0.7	198	10.86			X₇₂	1.0~1.5	440	24.12
		X₁₄	0.7~0.8	313	17.17			X₇₃	1.5~2.0	421	23.10
		X₁₅	0.8~0.9	195	10.70			X₇₄	2~3	251	13.74
		X₁₆	0.9~1.0	514	28.18			X₇₅	3~4	138	7.54
	CW	参照	0~0.2	295	16.20			X₇₆	>4	85	4.65
		X₂₁	0.2~0.3	238	13.05		坡度变化	X₁₂₁	连续上坡	268	14.71
		X₂₂	0.3~0.4	122	6.68			X₁₂₂	连续下坡	899	49.30
		X₂₃	0.4~0.5	236	12.94			参照	先上坡后下坡再上坡	62	3.42
		X₂₄	0.5~0.6	234	12.83			X₁₂₃	先下坡后上坡再下坡	70	3.85
		X₂₅	0.6~0.7	219	12.03			X₁₂₄	先上坡后连续下坡	204	11.18
		X₂₆	0.7~0.9	174	9.52			X₁₂₅	先下坡后连续上坡	75	4.12
		X₂₇	>0.9	305	16.74			X₁₂₆	先连续上坡后下坡	173	9.47
	VRC/ ((°)· km^-1)	参照	0 ~30	175	9.57			X₁₂₇	先连续下坡后上坡	72	3.96
		X₃₁	30~40	253	13.85	平纵线形组合	CSL/ (%· km^-1)	X₄₁	<1	395	21.66
		X₃₂	40 ~50	322	17.65			参照	1 ~1.5	225	12.35
		X₃₃	50 ~60	229	12.57			X₄₂	1.5 ~2	264	14.49
		X₃₄	60 ~70	179	9.79			X₄₃	2 ~2.5	215	11.76
		X₃₅	70 ~80	98	5.40			X₄₄	2.5 ~3	195	10.70
		X₃₆	80 ~100	110	6.04			X₄₅	3 ~4	226	12.41
		X₃₇	>100	458	25.13			X₄₆	>4	303	16.63
	CRF	X₈₁	0~0.3	248	13.58		CSR/ (%· km^-1)	参照	<1	231	12.67
		X₈₂	0.3~0.6	370	20.27			X₅₁	1 ~2	254	13.90
		X₈₃	0.6~0.9	588	32.25			X₅₂	2 ~3	343	18.82
		参照	0.9~1.2	536	29.41			X₅₃	3 ~4	244	13.37
		X₈₄	>1.2	82	4.49			X₅₄	4 ~5	50	2.73
	CRB	X₉₁	0~0.3	263	14.44			X₅₅	5 ~6	149	8.18
		X₉₂	0.3~0.6	340	18.66			X₅₆	6 ~10	186	10.21
		X₉₃	0.6~0.9	481	26.36			X₅₇	>10	367	20.11
		参照	0.9~1.2	624	34.22		VHR	X₁₀₁	0~10	234	12.83
		X₉₄	>1.2	115	6.31			X₁₀₂	10~15	252	13.80
	CT	X₁₁₁	1	646	35.40			X₁₀₃	15~20	186	10.21
	CT	参照	0	1 178	64.60			X₁₀₄	20~30	245	13.42
纵面线形组合	ΔS₁/%	参照	>0.5	127	6.95			X₁₀₅	30~40	193	10.59
		X₆₁	0.5~1.0	400	21.93			X₁₀₆	40~50	251	13.74
		X₆₂	1.0~1.5	397	21.76			参照	>50	463	25.40

表1

表2

图2

图3

图4

表3

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事故严重程度	变量	变量定义	参数估计	标准差
仅财产损失事故Y₁	X₁₃	ROC=0.5~0.6	1.314^***	0.198
	X₂₃	CW=0.4~0.5	1.117^***	0.184
	X₄₆/(%·km^-1)	CSL>4	-1.182^***	0.223
	X₇₆/%	ΔS₂>4	0.913^**	0.341
	X₁₂₃	先下坡后上坡再下坡	0.786^**	0.366
轻伤事故Y₂	X₁₂	ROC=0.4~0.5	-1.911^***	0.405
	X₂₂(均值)	CW=0.3~0.4	-2.317^***	1.098
	X₂₂(标准差)	CW=0.3~0.4	3.185^***	1.155
	X₂₅	CW=0.6~0.7	1.416^***	0.303
	X₄₅/(%·km^-1)	CSL=3~4	-1.608^***	0.296
	X₆₄/%	ΔS₁=2~3	-1.968^***	0.274
	X₆₅/%	ΔS₁=3~4	-0.727^**	0.294
	X₇₅/%	ΔS₂=3~4	-1.493^***	0.264
	X₁₂₁	连续上坡	0.843^***	0.227
重伤或死亡事故Y₃	ASC_S3	常数项	-1.863^***	0.132
	X₁₄	ROC=0.7~0.8	-0.947^***	0.362
	X₃₆/((°)· km^-1)	VRC=80~100	0.648^*	0.359
	X₅₇/(%·km^-1)	CSR>10	0.476^*	0.275
	X₈₁	CRF<0.3	0.346^***	0.350
	X₉₂	CRB=0.3~0.6	0.538^**	0.229

拟合优度指标	随机参数logit模型
LL(0)	-2 048.91
LL(β)	-1 067.78
χ²	1 962.26
AIC	2 175.56
BIC	2 286.23
Mcfadden Pseudo R²	0.48