基于微观动力学参数的高速公路特征路段事故风险分析

doi:10.13229/j.cnki.jdxbgxb.20220247

摘要/Abstract

摘要：

为剖析高速公路不同特征路段的致险机理，在研究中引入了车辆正常行驶时的微观动力学参数，分别构建了标准有序Logit模型和二项Logit模型定量分析微观动力学参数与特征路段（分流区、合流区、曲线段等），以及微观动力学参数与事故风险之间的关系，并进一步基于模型推导分析了不同特征路段对事故风险独立的作用效能。结果表明：纵向动力学因子可以映射路段追尾事故风险的高低，而不同特征路段又可以使纵向动力学因子发生不同倾向的浮动，即可以将该因子作为风险传递的中间变量，来判定路段事故风险的高低；相较于“无分流区的直线段”，“分流区”这一复杂特征对追尾事故风险的增益作用最大，“复合线形”次之，“平曲线”最弱，且平曲线路段的缓和曲线长度越大，追尾事故风险越高；发现了显著的风险补偿现象：较为轻松的驾驶环境可能会导致驾驶员放松警觉，引起追尾事故风险上升。本文的结论可以为高速公路不同特征路段的事故预防、针对某一特定类型事故的救援措施制定提供理论支撑和经验借鉴。

关键词: 交通运输安全工程, 道路事故风险, 标准有序Logit模型, 二项Logit模型, 微观动力学参数, 道路复杂特征

Abstract:

To analyze the risk-causing mechanism of different freeway characteristics， this paper introduced the microscopic kinetic parameters of the vehicle under normal driving conditions. The standard ordered Logit model and the binary Logit model were conducted to explore the relationship between the microscopic kinetic parameters and road characteristics （e.g.， diversion areas， merging areas， and curve segments）； and the relationship between the microscopic kinetic parameters and crash risk （e.g. rear-end， fixed-object hitting， and overturning）. Then， taking the micro-kinetic parameters as the link， the effects of different road characteristics on crash risk is independently quantified. The results indicate that the longitudinal kinetic factorcan effectively map the level of rear-end risk， and the difference in fluctuations of can be attributed to the diversity of road characteristics. That is， the longitudinal kinetic factor can be used as an intermediate variable of risk transmission to realize the determination of the level of crash risk on freeways， and at the same time， provide help for the identification of road dangerous defects. Compared with the straight-line section without a diversion area， the complex feature of the "diversion area" has the greatest effect on the added risk of rear-end crashes， followed by "composite alignment"， and the "horizontal curve" is the weakest. The greater the length of the transition curve of the horizontal curve section， the higher the risk of rear-end crashes. In addition， the paper also finds a significant risk compensation phenomenon， suggesting that a more relaxed driving environment may cause drivers to relax their vigilance and increase the risk of rear-end. The conclusions can provide theoretical support and experience for the prevention of crashes and the formulation of rescue measures for a specific type of crash.

Key words: traffic and transportation safety engineering, road crash risk, standard ordered Logit model, binary Logit model, micro-kinetic parameters, freeway characteristics

中图分类号:

U491.3

何杰,张长健,严欣彤,王琛玮,叶云涛. 基于微观动力学参数的高速公路特征路段事故风险分析[J]. 吉林大学学报(工学版), 2024, 54(1): 162-172.

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.

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采集时间/s	F_x /kN	F_y /kN	F_z /kN	T_x /（N·m）	T_y /（N·m）	T_z /（N·m）	V/（km·h^-1）
0	-0.61	-0.13	4.58	0.12	0.08	0.014	105.91
0.01	-0.36	-0.05	4.59	0.12	0.08	0.017	106.12
0.02	-0.44	0.07	4.85	0.07	0.08	0.029	106.46
0.03	-0.54	0.02	4.65	0.11	0.09	-0.006	106.32
0.04	-0.35	0.07	4.68	0.11	0.08	0.022	105.98
0.05	-0.37	-0.13	4.53	0.13	0.09	0.010	105.98
0.06	-0.62	-0.06	4.62	0.10	0.08	0.000	105.98
0.07	-0.62	-0.17	4.34	0.15	0.08	-0.004	106.39
0.08	-0.41	-0.08	4.48	0.12	0.09	0.035	106.53
0.09	-0.34	0.02	4.50	0.12	0.08	0.030	106.39
?							?

简称	道路特征	类型	变量描述
x₁	高程差	连续变量	-
x₂	高程标准差	连续变量	-
x₃	道路线形	分类变量	x₃₀：直线段 x₃₁：平曲线路段 x₃₂：复合线形路段
x₄	平曲线半径	连续变量	取对数值
x₅	缓和曲线长度	连续变量	取对数值
x₆	隧道	分类变量	x₆₀：不含隧道 x₆₁：只含隧道出口 x₆₂：只含隧道入口 x₆₃：同时含有隧道出口和入口 x₆₄：在隧道内部
x₇	沿江桥	分类变量	x₇₀：无沿江桥 x₇₁：含有沿江桥
x₈	冲突区	分类变量	x₈₀：不含冲突区 x₈₁：只含有分流区 x₈₂：只含有合流区 x₈₃：同时含有分流区和合流区

解释变量	F_x_，std	F_y_，std	F_z_，std	T_x_，std	T_y_，std	T_z_，std	V_std
x₁	-	-	-	-	-	-	-
x₂	0.072	-	-	-0.069	-	-	-
x₃（以x₃₀为参考）
x₃₁	-	-1.590	-	-1.512	-1.735	-	-
x₃₂	-	-	-	-	-1.939	-	-
x₄	-	-1.142	-	-0.900	-	-0.833	-
x₅	-	-0.298	-	-	-	-	-0.290
x₆（以x₆₀为参考）
x₆₁	-	-	-	1.145	-	-	-
x₆₂	-	-	-	-	-	-	-
x₆₃	-	-	1.440	-	-	-	-
x₆₄	-	-		-	-	-	-
x₇	1.056	0.993	1.379	-	-	-	-
x₈（以x₈₀为参考）
x₈₁	-	-	-	-	-2.076	-	-
x₈₂	-	2.14	-	-	-	-	-
x₈₃	2.690	2.071	2.948	-	-	-	-
u₁	-0.142	-11.146	0.113	-9.322	-10.905	-8.443	-6.725
u₂	1.487	-9.398	1.774	-7.644	-9.249	-6.715	-5.086

	横向动力学因子	纵向动力学因子
F_x，_std	0.473	0.066
F_y，_std	0.743	0.363
T_x，_std	0.677	0.162
T_y，_std	0.218	0.835
T_z，_std	0.712	0.287
V_std	0.196	0.826

变量	追尾事故		撞固定物事故		翻车事故
变量	系数	P值	系数	P值	系数	P值
截距	-0.841	***	-0.170	-	-0.468	**
横向动力学因子	-0.281	-	0.010	-	0.098	-
纵向动力学因子	-0.741	***	0.097	-	0.143	-