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

Abstract

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

CLC Number:

U491

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.

Figures/Tables 4

Fig.1

Table 1

Collision classification and risk index range"

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）

Table 1

Table 2

Table 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

Evaluating and forecasting rear⁃end collision risk of long longitudinal gradient roadway via traffic conflict

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