基于生存分析的山区公路借道超车风险暴露时间模型

doi:10.13229/j.cnki.jdxbgxb.20250170

摘要/Abstract

摘要：

针对山区双车道公路超车车辆与被超车车辆交互、与对向来车交互两种场景，利用两台无人机同步采集借道超车行为视频，通过改进二维碰撞时间（TTC）计算方法量化借道超车风险；基于生存分析理论构建了考虑驾驶风格的借道超车风险暴露时间模型，解析了超车车辆与不同交互车辆间的风险持续性影响机制。结果表明：相较于与对向来车交互，超车车辆与被超车车辆交互的平均风险暴露时间更长（5.19 s）；超速型风格驾驶人的风险暴露时间较保守型增加15%，超越货车时的风险暴露时间较超越摩托车和小客车分别增加19%和42%；除超车车辆速度外，两车横向位置差、对向来车速度分别对两种借道超车交互场景风险暴露时间的平均影响程度最大。

关键词: 交通运输安全工程, 超车风险暴露时间, 生存分析, 山区公路, 驾驶风格

Abstract:

In order to investigate the mechanism of risk persistence effects between overtaking vehicles and different interacting vehicles， two UAVs （Unmanned Aerial Vehicles） were used to collect video data of overtaking behaviors on a mountainous highway. The two-dimensional time to collision （TTC） calculation method was improved to assess overtaking risk. And an accelerated failure time （AFT） model was developed based on the survival analysis theory， considering the differences in overtaking styles. The findings indicate that the average risk exposure duration was considerably extended （5.19 s） when the overtaking vehicle interacted with the overtaken vehicles， in contrast to interactions with oncoming vehicles in the opposite direction. Speeding driving style increased the risk exposure duration by 15% compared to conservative driving style. When the overtaken vehicle was a truck， the risk exposure duration increased by 19% and 42% compared to motorcycles and minibuses， respectively. In addition to the overtaking vehicle's speed， the lateral position difference between the two vehicles and the oncoming vehicle's speed had the greatest average effect on the risk exposure duration for the different overtaking scenarios.

Key words: traffic and transportation safety engineering, overtaking risk exposure duration, survival analysis, mountainous highways, driving style

中图分类号:

U491.31

卢梦媛,戢晓峰,徐迎豪,覃文文. 基于生存分析的山区公路借道超车风险暴露时间模型[J]. 吉林大学学报(工学版), 2026, 56(9): 2361-2372.

Meng-yuan LU,Xiao-feng JI,Ying-hao XU,Wen-wen QIN. Overtaking risk exposure duration model of two⁃lane mountainous highways based on survival analysis[J]. Journal of Jilin University(Engineering and Technology Edition), 2026, 56(9): 2361-2372.

图/表 17

图1

图2

图3

图4

图5

表1

图6

表2

图7

表3

图8

表4

借道超车风险暴露时间协变量描述性统计"

协变量	超车场景1			超车场景2
协变量	协变量说明	平均值	标准差	协变量说明	平均值	标准差
$Z 1$	超车驾驶人驾驶风格类型（0~3分别代表保守型、稳健型、超速型、激进型）	-	-	超车驾驶人驾驶风格类型（0~3分别代表保守型、稳健型、超速型、激进型）	-	-
$Z 2$	被超车车辆类型（0~2分别代表小客车、摩托车、货车）	-	-	被超车车辆类型（0、1分别代表摩托车、货车）	-	-
$Z 3$	超车路段类型（0、1分别代表平直、弯道）	-	-	-	-	-
$v i$ /（m·s^-1）	超车车辆速度	17.64	5.62	超车车辆速度	14.43	6.43
$a i$ /（m·s^-2）	超车车辆加速度	0.28	0.53	超车车辆加速度	0.16	0.48
$c v i$	超车车辆速度变异系数	0.16	0.26	超车车辆速度变异系数	0.14	0.14
$v j$ /（m·s^-1）	被超车车辆速度	13.44	4.65	对向来车速度	15.10	5.85
$a j$ /（m·s^-2）	被超车车辆加速度	0.03	0.36	对向来车加速度	-0.17	0.59
$c v j$	被超车车辆速度变异系数	0.09	0.10	对向来车速度变异系数	0.15	0.17
$Δ x i j$ /m	两车横向位置差	2.41	0.60	两车横向位置差	2.62	1.2
$Δ v i j$ /（m·s^-1）	两车速度差	4.53	3.11	两车速度差	5.33	3.39
$s i j$ /m	两车车头间距	17.92	7.32	两车车头间距	36.34	17.90
$t i j T W H$ /s	两车车头时距	1.40	0.85	两车车头时距	3.14	1.25

表4

表5

表6

Weibull AFT模型协变量参数估计（场景1）"

协变量	β	exp（β）	exp（β）-1	SE	z	p-value	95% CI
协变量	β	exp（β）	exp（β）-1	SE	z	p-value	Lower	Upper
$Z 1 = 1$	0.00	1.00	0.00	0.05	0.06	0.95	-0.09	0.09
$Z 1 = 2$	0.14	1.15	0.15	0.05	2.61	0.01	0.04	0.25
$Z 1 = 3$	0.03	1.03	0.03	0.05	0.57	0.57	-0.07	0.13
$Z 2 = 1$	0.00	1.00	0.00	0.06	0.04	0.97	-0.11	0.11
$Z 2 = 2$	0.35	1.42	0.42	0.03	11.61	<0.005	0.29	0.41
$Z 3 = 1$	-0.20	0.82	-0.18	0.06	-3.38	<0.005	-0.32	-0.08
$v i$	-0.02	0.98	-0.02	0.00	-3.91	<0.005	-0.03	-0.01
$a i$	-0.12	0.88	-0.12	0.02	-5.15	<0.005	-0.17	-0.08
$c v j j = 1$	0.51	1.67	0.67	0.14	3.62	<0.005	-0.03	-0.01
$Δ x i j j = 1$	-0.10	0.91	-0.09	0.03	-3.71	<0.005	-0.15	-0.05
$Δ v i j j = 1$	0.01	1.01	0.01	0.00	2.42	0.02	0.00	0.02

表6

表7

Log-Logistic AFT模型协变量参数估计（场景2）"

协变量	β	exp（β）	exp（β）-1	SE	z	p-value	95% CI
协变量	β	exp（β）	exp（β）-1	SE	z	p-value	Lower	Upper
$Z 1 = 1$	-0.30	0.74	-0.26	0.18	-2.35	0.02	-0.55	-0.05
$Z 1 = 2$	0.15	1.16	0.16	0.13	0.88	0.38	-0.19	0.49
$Z 1 = 3$	-0.20	0.82	-0.18	0.17	-1.44	0.15	-0.48	0.07
$Z 2 = 1$	0.17	1.19	0.19	0.14	2.24	0.02	0.02	0.32
$v i$	-0.06	0.94	-0.06	0.08	-5.30	<0.005	-0.09	-0.04
$v j j = 2$	0.05	1.05	0.05	0.01	5.49	<0.005	0.03	0.07
$s i j j = 2$	-0.02	0.98	-0.02	0.00	-5.79	<0.005	-0.03	-0.01

表7

图9

图10

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超车交互场景	被超车车辆类型	ITTC_2D	TTC_2D	TTC _x	TTC _y
场景1	总体	7.14	10.00	11.34	12.27
	摩托车	6.58	8.52	9.47	10.80
	小客车	8.00	10.67	12.13	11.55
	货车	4.70	7.98	9.28	15.28
场景2	总体	3.91	8.34	3.72	17.18
	摩托车	4.33	6.38	3.63	19.41
	货车	3.75	14.51	3.99	12.21

超车交互场景	所处超车阶段	风险暴露时间/s				风险暴露水平
超车交互场景	所处超车阶段	平均值	标准差	最小值	最大值	平均值	标准差	最小值	最大值
场景1	全过程	5.19	1.87	0.00	13.17	0.37	0.16	0.00	0.96
	驶出换道	1.03	1.45	0.00	7.17	0.33	0.39	0.00	1.00
	占道超车	2.76	2.13	0.00	8.67	0.43	0.31	0.00	1.00
	驶回换道	1.41	2.00	0.00	7.83	0.26	0.29	0.00	1.00
场景2	全过程	3.05	2.00	0.00	6.00	0.17	0.11	0.00	0.41
	驶出换道	2.14	2.06	0.00	5.33	0.27	0.30	0.00	1.00
	占道超车	0.45	0.73	0.00	2.50	0.15	0.27	0.00	1.00
	驶回换道	0.46	0.79	0.00	3.00	0.16	0.29	0.00	1.00

特征参数	Cluster 0	Cluster 1	Cluster 2	Cluster 3
TOR	0.79	0.47	0.82	0.74
LCV	0.20	0.14	0.18	0.65
OTR	0.02	0.02	0.91	0.04

超车交互场景	指标	Weibull 分布	Log-Logistic分布	Log-Normal分布
场景1	AIC	1 398.33	1 527.30	1 769.14
场景1	BIC	1 449.35	1 578.32	1 820.15
场景2	AIC	191.56	181.16	190.33
场景2	BIC	210.71	200.30	209.48