Journal of Jilin University(Engineering and Technology Edition) ›› 2023, Vol. 53 ›› Issue (5): 1355-1363.doi: 10.13229/j.cnki.jdxbgxb.20210912

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

Heng-yan PAN1(),Yong-gang WANG1(),De-lin LI1,Jun-xian CHEN1,Jie SONG1,Yu-quan YANG2   

  1. 1.College of Transportation Engineering,Chang'an University,Xi'an 710018,China
    2.School of Traffic and Transportation,Northeast Forestry University,Harbin 150040,China
  • Received:2021-09-13 Online:2023-05-01 Published:2023-05-25
  • Contact: Yong-gang WANG E-mail:HyPan7@chd.edu.cn;wangyg@chd.edu.cn

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

Fig.1

Status of the longitudinal gradient section and video screenshot"

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)

无冲突,TTC≥5.2/

0<pr0.003

一般冲突,2.5≤TTC<5.2

0.003<pr0.723

严重冲突,TTC<2.5/

0.723<pr<1

无事故,E=0 J,cr=0000

轻微后果,0<E≤37 500 J,

0<cr0.33

(0,0.1992](0.0012,0.4872](0.2892,0.5980)

一般后果,37 500<E≤75 000 J,

0.33<cr0.67

(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<cr<1

(0.4020,0.6012](0.4032,0.8892](0.6912,1)
严重后果,E≥112 500 J,cr=1(0.6,0.601](0.601,0.8892](0.8892,1)

Table 2

Results of parameter estimation by ZINB regression"

上坡下坡
指标Coef.Std. Err.zP>zCoef.Std. Err.zP>z
Negative binomial
Q0.750.1544.8600.8480.1425.970
Pro-0.0060.085-0.070.941-0.0030.1-0.030.977
_cons-1.7710.296-5.980-1.4490.289-5.020
Inflate
v.m0.1580.3680.430.6670.2570.6160.420.677
v.s-2.4530.615-3.990-4.3481.31-3.320.001
a.m0.6380.4911.30.194-0.2340.474-0.490.621
a.s-2.2010.734-30.003-4.7671.807-2.640.008
h.m1.5650.4313.6304.5991.4223.230.001
h.s-0.150.41-0.370.715-0.6670.82-0.810.416
_cons5.6721.1055.13014.2794.0493.530
/lnalpha-20.496430.998-0.050.962-24.545499.651-0.050.961
alpha1.26E-095.41E-072.19E-111.09E-08
Log-Lik Full Model-166.301-217.687
McFadden's R20.5840.507
McFadden's Adj R20.5560.482

Table 3

Significance analysis of risk level of rear-end collisions and traffic flow characteristics indicators"

指标上坡下坡
Coef.Std. Err.zP>zCoef.Std. Err.zP>z
v.m0.5600.0926.1100.0000.7240.0927.8500.000
v.s0.6430.1046.1700.0000.8230.1186.9900.000
a.m0.7390.0937.9800.000-0.7080.091-7.7800.000
a.s0.7570.0928.2100.0000.6840.0927.4400.000
h.m-0.6180.098-6.3300.000-0.6480.098-6.6100.000
h.s0.0410.0470.8700.386-0.0210.046-0.4600.646
Q0.4070.1004.0700.0000.2710.1072.5200.012
Pro0.6200.0946.5700.0000.4380.0954.6300.000
/cut17.2310.4377.1670.436
/cut213.7420.74513.7630.758
Prob>chi200
Pseudo R20.64300.6315
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