考虑多模式失效概率的长下坡路段重型卡车事故预测模型

尹燕娜; 温惠英

doi:10.3963/j.jssn.1674-4861.2022.03.001

考虑多模式失效概率的长下坡路段重型卡车事故预测模型

doi: 10.3963/j.jssn.1674-4861.2022.03.001

尹燕娜,
温惠英^,

华南理工大学土木与交通学院广州 510641

基金项目:

国家自然科学基金项目 52172345

详细信息

作者简介:
尹燕娜(1987—)，博士研究生. 研究方向：道路交通安全. E-mail：1241085280@qq.com

通讯作者:
温惠英(1965—)，博士，教授. 研究方向：道路交通安全、交通规划等. E-mail：hywen@scut.edu.cn

中图分类号: U491.31
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出版历程
- 收稿日期: 2022-01-01
- 网络出版日期: 2022-07-25

Development of Crash Prediction Models Involving Heavy-duty Trucks over Long Downhill Segments Considering Multi-mode Failure Probability

YIN Yanna,
WEN Huiying^,

School of civil engineering and transportation, South China University of Technology, Guangzhou 510641, China

摘要

摘要: 为挖掘多模式失效概率与长下坡路段重型卡车事故之间的关系，建立了重型卡车在长下坡路段的多模式失效概率与车辆事故之间的关系模型。并针对重型卡车在长下坡路段可能的失效模式，如侧滑、侧翻、视距不足、制动失效，在此基础上建立了多模式失效概率预测模型；通过蒙特卡罗法模拟并求解单模式失效的概率，宽界限法求解失效系统的多模式失效概率；将多模式失效概率作为解释变量与其他道路因素结合，分别建立泊松模型、随机效应泊松模型、随机参数泊松模型，将多模式失效概率与重型卡车事故建立函数关系；对比3种模型的拟合优度指标，优选出最优事故预测模型，用来挖掘重型卡车事故与多模式失效概率之间的关系。以华盛顿州71段长下坡10年的重型卡车事故数据及道路设计数据进行方法验证。结果表明：随机参数泊松模型与随机效应泊松模型的拟合优度相差较小，二者均优于泊松模型；当考虑多模式失效概率时，平曲线半径、纵坡坡度、超高对重型卡车事故的影响均不显著，即三者的影响被削弱，尤其是平曲线半径和超高，多模式失效概率的弹性(0.239)远大于二者的弹性(平曲线半径和超高的弹性分别仅为0.097和0.002)；重型卡车的事故与多模式失效概率近似线性关系，且截距不为0。即多模式失效概率可用于道路安全分析的表征指标，但与事故概率不等价。
- 交通安全 /
- 长下坡路段 /
- 多模式失效概率 /
- 随机效应泊松模型 /
- 随机参数泊松模型 /
- 重型卡车
Abstract: A crash prediction model is developed, in order to explore the relationship between multi-mode failure probability and heavy-duty truck crashes over long downhill road sections. A model for multi-mode failure probability prediction is developed to study the probability of different types of failures associated with heavy-duty trucks, such as skidding, rollover, insufficient sight distance, and braking failure, on the long downhill sections. The single-mode failure probability is simulated using a Monte Carlo method and the multi-mode failure probability of the system is studied by a wide bound method. Three crash prediction models including a Poisson model, a random-effect Poisson model, and a random-parameter Poisson model are developed, considering multi-mode failure probability as one of the explanatory variables together with other impact factors. The models are used to link the multi-mode failure probability with the crashes of heavy-duty trucks. The optimal crash prediction model is selected through the goodness-of-fit for accurately modeling the relationship between crashes of the trucks and their multi-mode failure probability. The method is verified by a 10-year data of heavy-duty truck crash and road design of 71 long downhill sections in the Washington State, the United States. The results show that there is little difference in the goodness of fit between the random-effect Poisson model and random-parameter Poisson model, and both of them are better than the Poisson model. It is found that radius of the horizontal curves, grades and superelevation rates are not significant in leading to the crashes, when compared with the multi-mode failure probability. Study results show that, the elasticity of multi-mode failure probability (0.239) is much greater than that of the radius of horizontal curve and superelevation (0.097 and 0.002) respectively; heavy-duty truck crashes and multi-mode failure probability are approximately linearly correlated, and the intercept of the model is found to be other than "0". The above results indicate that the multi-mode failure probability can be used for road safety analysis, but it is not equivalent to the crash probability, which may be used as a basis for improving road design.
- traffic safety /
- long downhill segments /
- multi-mode failure probability /
- random effects Poisson model /
- random parameters Poisson model /
- heavy-duty truck

HTML全文

图 1 车辆在弯坡路段的受力分析

Figure 1. Force analysis of vehicles on combination sections of vertical and horizontal curve

下载: 全尺寸图片幻灯片

图 2 预测事故数与实际事故数比较

Figure 2. Comparison between predicted and actual crashes value

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图 3 重型卡车事故数与多模式失效概率的关系

Figure 3. Relationship between heavy truck crash values and multi-mode failure probability

下载: 全尺寸图片幻灯片

表 1 固定变量及取值

Table 1. Fixed variables and values

符号	变量解释	变量值
m	车辆的总重量/kg	20 000
m₀	满载质量/kg	30 000
T₀	制动器的初始温度/℃	本文取130
g	重力加速度/(m/s²)	9.81
ρ_θ	空气密度/(kg/m³)	1.225 8
A₂	重型车辆的迎风面积/m²	6
κ	车辆的空气阻力系数	1
z₀	主传动比	5.833
z_v	各档传动比
q	后轮制动器数	4
η	传动效率	0.83
m₁	所有后轮承受质量之和/kg	总质量的90%
γ	制动力分配系数/%	27.2
R₁	后轮动力半径/m	0.515
R₂	后轮滚动半径/m	0.527
m₂	制动鼓质量/kg	62.268
A₁	制动鼓的表面积/m²	0.347
c	制动鼓的比热容/(J/kg·℃)	482
系数	τ₀ = m/m₀；τ₁ = 66.34；τ = 1.0475; τ₂ = 0.050 1
L	路段长度/km
h_r/h_g		0.25
r_φ	侧倾率	0.05
B/2h_g		0.58
μ₂	湿滑路面提供的附着系数	0.4

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表 2 变量的统计特征

Table 2. Statistical characteristics of variables

变量	均值	标准差	最大值	最小值
重型卡车事故数	9.085	14.602	80	0
暴露变量
路段长度/km	3.676	3.950	19.614	1.030
年平均日交通量/(辆/天)	146 33	250 64	165 789	531
交通情况变量
法定限速/(km/h)	84.416	15.623	112.63	40.225
卡车的百分比/%	12.788	8.959	38.949	0.184
道路设计参数
平曲线半径/m	0.416	0.438	2.329	0.011
纵坡/%	3.996	0.815	6.478	2.804
超高/%	1.646	3.231	10	0
多模式失效概率	0.391	0.311	1	0

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表 3 考虑多模式失效概率的随机效应泊松模型估计结果

Table 3. estimation results of crash prediction model considering multi-mode failure probability

变里名称	泊松模型			随机效应泊松模型			随机参数泊松模型
变里名称	系数值	标准误	P值	系数值	标准误	P值	系数值	标准误	P值	弹性
截距项	-9.595***	0.711	0	-8.769***	0.711		-8.315***	0.719	0
参数分布标准差				0.585***	0.049		0.250***	0.042
暴露变量
路段长度的对数	0.124***	0.011	0	0.142***	0.012	0	0.138***	0.012	0	0.506
年平均日交通量对数	0.967***	0.055	0	0.941***	0.058	0	0.903***	0.059	0	7.805
参数分布标准差							0.057***	0.005
交通情况变量
法定限速	0.015***	0.004	0	0.006*	0.004	0.094	0.005	0.004	0.250	0.383
卡车的百分比	0.018**	0.009	0.037	0.029***	0.009	0.001	0.032***	0.009	0.001	0.412
道路设计参数
平曲线半径	-0.11	0.144	0.442	0.167	0.15	0.265	0.234	0.151	0.123	0.097
纵坡	0.182**	0.074	0.014	0.077	0.086	0.369	0.067	0.087	0.441	0.267
超高	-0.046***	0.015	0.002	-0.007	0.016	0.642	0.001	0.016	0.934	0.002
多模式失效概率	0.459**	0.2	0.022	0.541**	0.212	0.011	0.610***	0.215	0.005	0.239
样本数量	71			71			71
参数数量	9			10			11
对数似然值	-232.031			-183.496			-182.502
仅含常数项的对数似然值	-643.079			-1653.35			-1653.35
麦克费登，	0.639			0.889			0.89
A/C	482.1			387			387
MAD	4.077			1.02			1.012
均方根误差	6.613			1.346			1.316
注：*，，* 分别表示在1%、5%和10%水平上显著。

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