基于交通冲突的长纵坡路段追尾风险评估及预测

doi:10.13229/j.cnki.jdxbgxb.20210912

摘要/Abstract

摘要：

通过无人机航拍、雷达测速与人工现场调查对山区长纵坡路段的车辆轨迹与交通流数据进行采集。基于交通冲突技术给出长纵坡路段车辆碰撞时间及冲突所引发追尾事故概率的计算方法，基于动能守恒定律给出长纵坡路段追尾事故的碰撞动能损失量，分别用于量化车辆追尾冲突与事故后果的严重性。构建了追尾风险指数，用于评价长纵坡路段的追尾风险程度。分别运用零膨胀负二项回归（ZINB）与有序Logistic回归对追尾冲突频次与追尾风险等级进行预测，并分析交通流特性对其产生的影响。结果得出：零膨胀负二项回归对上坡与下坡方向冲突频次的预测效果较好，R²分别为0.556与0.482；有序Logistic回归对上下坡追尾风险等级的预测效果极佳，R²分别约为0.643与0.632。车速、加速度、交通量与交通流车辆构成对冲突数量与追尾风险程度的影响存在差异。

关键词: 交通工程, 交通冲突技术, 长纵坡路段, 追尾事故风险, 零膨胀负二项回归, 有序Logistic回归

Abstract:

The vehicle trajectory and traffic flow data of the long longitudinal gradient roadway are collected through aerial photography by UAV， radar speed measurement and manual field survey. Based on the traffic conflict technology， the Time to Collision and the probability of rear-end collision caused by the conflict are calculated， and the loss of crash kinetic energy based on the law of conservation of kinetic energy is given for the rear-end collision on the long longitudinal gradient roadway， which are used to quantify the severity of vehicle rear-end conflict and collision consequences. The index of rear-end risk index is constructed to evaluate the degree of rear-end risk on long longitudinal gradient roadway. The zero-inflated negative binomial regression（ZINB） and ordered logistic regression were used to predict the frequency of rear-end conflicts and the risk level of rear-end， and analyze the influence of traffic flow characteristics indicators on them. The results showed that： the frequency of conflicts in uphill and downhill directions were well predicted with R² of 0.556 and 0.482， respectively； the risk level of rear-end collisions in uphill and downhill directions were well predicted with R² of 0.643 and 0.632， respectively； the effects of vehicle speed， acceleration， traffic volume and vehicle composition on the number of conflicts and the risk level of rear-end collisions were different.

Key words: traffic engineering, traffic conflict techniques, long longitudinal gradient roadway, rear-end collision risk, zero-inflated negative binomial regression, ordered Logistic regression

中图分类号:

U491

潘恒彦,王永岗,李德林,陈俊先,宋杰,杨钰泉. 基于交通冲突的长纵坡路段追尾风险评估及预测[J]. 吉林大学学报(工学版), 2023, 53(5): 1355-1363.

Heng-yan PAN,Yong-gang WANG,De-lin LI,Jun-xian CHEN,Jie SONG,Yu-quan YANG. Evaluating and forecasting rear⁃end collision risk of long longitudinal gradient roadway via traffic conflict[J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(5): 1355-1363.

图/表 4

图1

表1

事故分类及风险指数范围"

事故类型和E取值范围	冲突类型及TTC取值范围（下坡/上坡）
事故类型和E取值范围	无冲突，TTC≥5.2/ $0 < p r ≤ 0.003$	一般冲突，2.5≤TTC<5.2 $0.003 < p r ≤ 0.723$	严重冲突，TTC<2.5/ $0.723 < p r < 1$
无事故，E=0 J， $c r = 0$	0	0	0
轻微后果，0<E≤37 500 J， $0 < c r ≤ 0.33$	（0，0.1992］	（0.0012，0.4872］	（0.2892，0.5980）
一般后果，37 500<E≤75 000 J， $0.33 < c r ≤ 0.67$	（0.198，0.4032］	（0.1992，0.6912］	（0.4872，0.8020）
较严重后果，75 000 J<E< 112 500J， $0.67 < c r < 1$	（0.4020，0.6012］	（0.4032，0.8892］	（0.6912，1）
严重后果，E≥112 500 J， $c r = 1$	（0.6，0.601］	（0.601，0.8892］	（0.8892，1）

表1

表2

表3

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	上坡				下坡
指标	Coef.	Std. Err.	z	P>z	Coef.	Std. Err.	z	P>z
Negative binomial
Q	0.75	0.154	4.86	0	0.848	0.142	5.97	0
Pro	-0.006	0.085	-0.07	0.941	-0.003	0.1	-0.03	0.977
_cons	-1.771	0.296	-5.98	0	-1.449	0.289	-5.02	0
Inflate
v.m	0.158	0.368	0.43	0.667	0.257	0.616	0.42	0.677
v.s	-2.453	0.615	-3.99	0	-4.348	1.31	-3.32	0.001
a.m	0.638	0.491	1.3	0.194	-0.234	0.474	-0.49	0.621
a.s	-2.201	0.734	-3	0.003	-4.767	1.807	-2.64	0.008
h.m	1.565	0.431	3.63	0	4.599	1.422	3.23	0.001
h.s	-0.15	0.41	-0.37	0.715	-0.667	0.82	-0.81	0.416
_cons	5.672	1.105	5.13	0	14.279	4.049	3.53	0
/lnalpha	-20.496	430.998	-0.05	0.962	-24.545	499.651	-0.05	0.961
alpha	1.26E-09	5.41E-07			2.19E-11	1.09E-08
Log-Lik Full Model：	-166.301				-217.687
McFadden's R²：	0.584				0.507
McFadden's Adj R²：	0.556				0.482

指标	上坡				下坡
指标	Coef.	Std. Err.	z	P>z	Coef.	Std. Err.	z	P>z
v.m	0.560	0.092	6.110	0.000	0.724	0.092	7.850	0.000
v.s	0.643	0.104	6.170	0.000	0.823	0.118	6.990	0.000
a.m	0.739	0.093	7.980	0.000	-0.708	0.091	-7.780	0.000
a.s	0.757	0.092	8.210	0.000	0.684	0.092	7.440	0.000
h.m	-0.618	0.098	-6.330	0.000	-0.648	0.098	-6.610	0.000
h.s	0.041	0.047	0.870	0.386	-0.021	0.046	-0.460	0.646
Q	0.407	0.100	4.070	0.000	0.271	0.107	2.520	0.012
Pro	0.620	0.094	6.570	0.000	0.438	0.095	4.630	0.000
/cut1	7.231	0.437			7.167	0.436
/cut2	13.742	0.745			13.763	0.758
Prob>chi2	0				0
Pseudo R²	0.6430				0.6315

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