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

Abstract

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

CLC Number:

U491.3

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.

Figures/Tables 11

Fig.1

Fig.2

Table 1

Table 2

Table 3

Table 4

Table 5

Table 6

Fig.3

Fig.4

Table 7

References 23

1	World Health Statistics 2019. Monitoring health for the SDGs, sustainable development goals[R]. Geneva: World Health Organization, 2019.
2	张兰芳,赵焜. 考虑货车因素的高速公路短期交通流风险预测[J]. 同济大学学报: 自然科学版, 2018, 46(2): 66-72.
	Zhang Lan-fang, Zhao Kun. Short-term traffic flow risk prediction on freeways based on truck factors[J]. Journal of Tongji University(Natural Science), 2018, 46(2): 66-72.
3	孟祥海,侯芹忠,史永义,等. IHSDM高速公路事故预测模型[J]. 交通运输工程学报, 2016, 16(1): 123-132.
	Meng Xiang-hai, Hou Qin-zhong, Shi Yong-yi, et al. IHSDM freeway crash prediction models[J]. Journal of Traffic and Transportation Engineering, 2016, 16(1): 123-132.
4	赵跃峰,张生瑞,马壮林. 基于部分优势比的公路隧道交通事故严重程度分析模型[J]. 中国公路学报, 2018, 31(9): 159-166.
	Zhao Yue-feng, Zhang Sheng-rui, Ma Zhuang-lin. Analysis of traffic crash severity on highway tunnels using the partial proportion odds model[J]. China Journal of Highway and Transport, 2018, 31(9): 159-166.
5	王雪松,徐晓妍. 基于自然驾驶数据的危险事件识别方法[J]. 同济大学学报: 自然科学版, 2020, 48(1): 51-59.
	Wang Xue-song, Xu Xiao-yan. Detection of safety-critical events based on naturalistic driving data[J]. Journal of Tongji University(Natural Science), 2020, 48(1): 51-59.
6	Tefft B C. Reducing crash risk and improving traffic safety: research on driver behavior and performance[J]. Institute of Transportation Engineers, 2018, 88(8): 30-34.
7	海晶,金立生,关志伟,等. 驾驶风格对驾驶行为的影响[J]. 中国公路学报, 2018, 31(4): 18-27.
	Jing Hai, Jin Li-sheng, Guan Zhi-wei, et al. Effects of driving style on driver behavior[J]. China Journal of Highway and Transport, 2018, 31(4): 18-27.
8	Zhang C J, He J, King M, et al. A crash risk identification method for freeway segments with horizontal curvature based on real-time vehicle kinetic response[J]. Crash Analysis & Prevention, 2021, 150:No.105911.
9	王露,刘玉雯,陈红. 侧风下峡谷桥隧连接段汽车的行驶特性[J]. 吉林大学学报: 工学版, 2019, 49(3): 736-748.
	Wang Lu, Liu Yu-wen, Chen Hong. Cross-wind environment vehicle driving feature at canyon bridge and tunnel connection segment[J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(3): 736-748.
10	Chen J, Song J, Li L, et al. A novel pre-control method of vehicle dynamics stability based on critical stable velocity during transient steering maneuvering [J]. Chinese Journal of Mechanical Engineering, 2016, 29(3): 475-485.
11	李志刚,沈明,邹猛,等. 基于多体动力学的微型客车急转弯侧翻倾向性仿真[J]. 清华大学学报: 自然科学版, 2010, 50(8):1286-1289.
	Li Zhi-gang, Shen Ming, Zou Meng, et al. Multi-body dynamics simulations for mini-bus rollover tendency during sharp turns[J]. Journal of Tsinghua University(Science and Technology), 2010, 50(8):1286-1289.
12	吴德华,翁辉,何杰,等. 基于动力学仿真的龙泉~浦城高速公路设计方案比选[J]. 公路, 2013(9): 1-8.
	Wu De-hua, Weng Hui, He Jie, et al. Longpu expressway design scheme comparison based on the dynamic simulation method[J]. Highway, 2013(9): 1-8.
13	姜康,张梦雅,陈一锴. 山区圆曲线路段半挂汽车列车行驶安全性分析[J]. 交通运输工程学报, 2015(3): 109-117.
	Jiang Kang, Zhang Meng-ya, Chen Yi-kai. Driving safety analysis of semi-trailer train at circular curve section in mountain area[J]. Journal of Traffic and Transportation Engineering, 2015(3): 109-117.
14	Ferit Y. A multicriteria decision making-based methodology to identify crash-prone road sections[J]. Journal of Transportation Safety and Security, 2021, 13 (2): 143-157.
15	Liu Z Y, He J, Zhang C, et al. The impact of road alignment characteristics on different types of traffic crashs[J]. Journal of Transportation Safety & Security, 2020, 12 (5): 697-726.
16	Haghighi N, Porter R, Zhang G H, et al. Impact of roadway geometric features on crash severity on rural two-lane highways[J]. Crash Analysis & Prevention, 2018, 111: 34-42.
17	Mehri A, Sajedifar J, Abbasi M, et al. Safety evaluation of lighting at very long tunnels on the basis of visual adaptation[J]. Safety Science, 2019, 116: 196-207.
18	Zhou Z W, Ma J X, Lu T, et al. An evaluation method for visual search stability in urban tunnel entrance and exit sections based on markov chain[J]. IEEE Access, 2020, 8: 68559-68569.
19	邢璐. 基于微观轨迹数据的主线收费站分流区交通安全评价研究[D]. 南京: 东南大学交通学院, 2020.
	Xing Lu. The research of traffic safety evaluation on mainline toll plaza's diverging area using microcosmic trajectory data[D]. Nanjing: School of Transportation, Southeast University, 2020.
20	Yu R J, Abdel A M. Analyzing crash injury severity for a mountainous freeway incorporating real-time traffic and weather data[J]. Safety Science,2014, 63(1): 50-56.
21	张兰芳,王淑丽,陈程,等. 基于自然驾驶数据的高速公路出口区换道风险模型[J]. 同济大学学报:自然科学版, 2019, 47(10): 1446-1453.
	Zhang Lan-fang, Wang Shu-Li, Chen Cheng, et al. Lane-change risk model of freeway off-ramp areas based on naturalistic driving data[J]. Journal of Tongji University (Natural Science), 2019, 47(10): 1446-1453.
22	徐进,崔强,常旭,等. 苜蓿叶形互通立交进/出口的纵向驾驶行为特征[J]. 东南大学学报: 自然科学版, 2019, 49(6): 1205-1214.
	Xu Jin, Cui Qiang, Chang Xu, et al. Longitudinal driving behavior characteristics at approaching/departing areas of clover leaf interchange[J]. Journal of Southeast University(Natural Science Edition), 2019, 49(6): 1205-1214.
23	Rusli R, Haque M, King M, et al. Single-vehicle crashes along rural mountainous highways in Malaysia: an application of random parameters negative binomial model[J]. Crash Analysis and Prevention, 2017, 102:153-164.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

Comments

Recommended 0

No Suggested Reading articles found!

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	-

Analyzing traffic crash risk of freeway characteristics based on micro⁃kinetic parameters

RICH HTML

PDF (PC)

Abstract

Cite this article

share this article

Figures/Tables 11

References 23

Related Articles 4

Metrics

Comments

Recommended 0

序号	取值情况
1	x₃₁=0，x₃₂=0，x₈₁=0
2	x₃₁=1，x₃₂=0，x₈₁=0
3	x₃₁=0，x₃₂=1，x₈₁=0
4	x₃₁=0，x₃₂=0，x₈₁=1
5	x₃₁=1，x₃₂=0，x₈₁=1
6	x₃₁=0，x₃₂=1，x₈₁=1

[1]	ZONG Fang, LU Feng-rui, TANG Ming, LYU Jian-yu, WU Ting. Impact of habit and traffic condition on travel route selection [J]. 吉林大学学报(工学版), 2018, 48(4): 1023-1028.
[2]	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.
[3]	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.
[4]	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.

道路特征	纵向动力学因子		追尾事故风险
道路特征	T_y，_std	V_std	追尾事故风险
平曲线路段	减小	-	增加
复合线形路段	减小	-	增加
缓和曲线长度	-	减小	增加
分流区	减小	-	增加