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

Abstract

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

CLC Number:

U491.31

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.

Figures/Tables 17

Fig.1

Fig.2

Fig.3

Fig.4

Fig.5

Table 1

Fig.6

Table 2

Fig.7

Table 3

Fig.8

Table 4

Descriptive statistics of overtaking risk exposure duration"

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

Table 4

Table 5

Table 6

Estimates of Weibull AFT model （scenario 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

Table 6

Table 7

Estimates of Log-Logistic AFT model （scenario 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

Table 7

Fig.9

Fig.10

References 19

[1]	Mwesige G, Farah H, Koutsopoulos H N. Risk appraisal of passing zones on two-lane rural highways and policy applications[J]. Accident Analysis & Prevention, 2016, 90: 1-12.
[2]	Choudhari T, Budhkar A, Maji A. Modeling overtaking distance and time along two-lane undivided rural highways in mixed traffic condition[J]. Transportation Letters, 2022, 14(2): 75-83.
[3]	Branzi V, Meocci M, Domenichini L, et al. A combined simulation approach to evaluate overtaking behaviour on two-lane two-way rural roads[J]. Journal of Advanced Transportation, 2021(2): 1-18.
[4]	刘照霞, 付锐, 牛世峰. 基于极值理论与智能网联信息的超车风险评估[J]. 吉林大学学报: 工学版, 2025, 55(3): 925‑937.
	Liu Zhao-xia, Fu Rui, Niu Shi-feng,Risk assessment in overtaking scenarios using extreme value theory and intelligent and connected information[J]. Journal of Jilin University(Engineering and Technology Edition), 2025, 55(3): 925‑937.
[5]	Mahmud S M S, Ferreira L, Hoque Md S, et al. Overtaking risk modeling in two-lane two-way highway with heterogeneous traffic environment of a low-income country using naturalistic driving dataset[J]. Journal of Safety Research, 2022, 80: 380-390.
[6]	Li D, Pan H. Two-lane two-way overtaking decision model with driving style awareness based on a game-theoretic framework[J]. Transportmetrica A: Transport Science, 2023, 19(3): No.2076755.
[7]	Figueira A C, Larocca A P C. Analysis of the factors influencing overtaking in two-lane highways: a driving simulator study[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2020, 69: 38-48.
[8]	Pawar N M, Velaga N R. Investigating the influence of time pressure on overtaking maneuvers and crash risk[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2021, 82: 268-284.
[9]	Karimi A, Boroujerdian A M, Catani L, et al. Who overtakes more? Explanatory analysis of the characteristics of drivers from low/middle and high-income countries on passing frequency[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2021, 76: 167-177.
[10]	Papakostopoulos V, Nathanael D, Portouli E, et al. The effects of changes in the traffic scene during overtaking[J]. Accident Analysis & Prevention, 2015, 79: 126-132.
[11]	Song Y. Integrated overtaking model and safety analysis for truck platooning requirements on two-lane undivided highways[J]. Transportation Research Record, 2024, 2678(8): 1-18.
[12]	戢晓峰, 戴秉佑, 普永明, 等. 基于生存分析的山区双车道公路超车持续时间模型[J]. 交通运输系统工程与信息, 2022, 22(6): 183-190.
	Ji Xiao-feng, Dai Bing-you, Pu Yong-ming, et al. Overtaking duration model of two-lane mountainous highways based on survival analysis[J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(6): 183-190.
[13]	Venthuruthiyil S P, Chunchu M. Anticipated collision time (ACT): a two-dimensional surrogate safety indicator for trajectory-based proactive safety assessment[J]. Transportation Research Part C: Emerging Technologies, 2022, 139: No.103655.
[14]	Cheng H, Jiang Y, Zhang H, et al. Emergency index (EI): a two-dimensional surrogate safety measure considering vehicles' interaction depth[J]. Transportation Research Part C: Emerging Technologies, 2025, 171: No.104981.
[15]	Zang Y, Wen L, Cai P, et al. How drivers perform under different scenarios: ability-related driving style extraction for large-scale dataset[J]. Accident Analysis & Prevention, 2024, 196: No.107445.
[16]	Espinoza J, Delpiano R. Statistical models of interactions between vehicles during overtaking maneuvers[J]. Transportation Research Record: Journal of the Transportation Research Board, 2023,678(4):323-333.
[17]	Sabek B, Srour F J, El Mendelek M, et al. Are you in the mood to pass? A study on the interplay of psychological traits and traffic on young drivers' overtaking behavior on two-lane, two-way highways[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2024, 101: 59-77.
[18]	Ye S, Chen T, Oviedo-Trespalacios O, et al. Investigating work-related distraction's impact on male taxi driver safety: a hazard-based duration model[J]. Analytic Methods in Accident Research, 2024, 44: No.100350.
[19]	戢晓峰, 李金, 普永明, 等. 穿村镇公路车辆跟驰冲突暴露时间生存分析[J]. 交通运输系统工程与信息, 2025, 25(2): 349‑360.
	Ji Xiao-feng, Li Jin, Pu Yong-ming, et al. Survival analysis of car-following conflict exposure time on through-village highway[J]. Journal of Transportation Systems Engineering and Information Technology, 2025, 25(2): 349‑360.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

Comments

Recommended 0

No Suggested Reading articles found!

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

Overtaking risk exposure duration model of two⁃lane mountainous highways based on survival analysis

RICH HTML

PDF (PC)

Abstract

Cite this article

share this article

Figures/Tables 17

References 19

Related Articles 10

Metrics

Comments

Recommended 0

[1]	Tian-jun FENG,Yan-ming HAO,Fei-yan LI,He-yao GAO,Yi-xian LIU,Nan LIU,Jin-feng LI. Lane⁃changing model for connected and automated vehicle aggregation considering driving styles [J]. Journal of Jilin University(Engineering and Technology Edition), 2026, 56(7): 1834-1844.
[2]	Lan-fang ZHANG,Gen-ze LI,Ting-yu LIU,Bo YU. Mechanism and modeling of car⁃following behavior under local multi⁃vehicle influence [J]. Journal of Jilin University(Engineering and Technology Edition), 2025, 55(3): 963-973.
[3]	Xin-gang GUO,Song WANG,Chao CHENG,Zhen FAN. Combined game theory and driving style hybrid traffic flow lane change decision model [J]. Journal of Jilin University(Engineering and Technology Edition), 2025, 55(12): 3875-3884.
[4]	Xiao-feng JI,Ying-hao XU,Yong-ming PU,Jing-jing HAO,Wen-wen QIN. Risk prediction model of passenger car following behavior under truck movement interruption of two-lane highway in mountainous area [J]. Journal of Jilin University(Engineering and Technology Edition), 2024, 54(5): 1323-1331.
[5]	Jie HE,Chang-jian ZHANG,Xin-tong YAN,Chen-wei WANG,Yun-tao YE. Analyzing traffic crash risk of freeway characteristics based on micro⁃kinetic parameters [J]. Journal of Jilin University(Engineering and Technology Edition), 2024, 54(1): 162-172.
[6]	Ya-li ZHANG,Rui FU,Wei YUAN,Ying-shi GUO. Classification and recognition model of entering and leaving stops' driving style considering energy consumption [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(7): 2029-2042.
[7]	Yu FANG,Li-jun SUN. Urban bridge performance decay model based on survival analysis [J]. Journal of Jilin University(Engineering and Technology Edition), 2020, 50(2): 557-564.
[8]	SUN Lu, XU Jian, CUI Xiang-min. Panel data models for analysis and prediction of crash count [J]. 吉林大学学报(工学版), 2015, 45(6): 1771-1778.
[9]	XU Jian, SUN Lu. Modeling of excess zeros issue in crash count andysis [J]. 吉林大学学报(工学版), 2015, 45(3): 769-775.
[10]	JIN Li-sheng, WANG Yan, LIU Jing-hua, WANG Ya-li, ZHENG Yi. Front vehicle detection based on Adaboost algorithm in daytime [J]. 吉林大学学报(工学版), 2014, 44(6): 1604-1608.