合流区域多车交互风险实时评估方法

doi:10.13229/j.cnki.jdxbgxb20210148

Abstract

Abstract:

To explore the traffic safety characteristics of the multi-vehicle interactive lane-changing at the merging area， the study of real-time multi-vehicle interactive risk assessment was carried out. Considering the characteristics of vehicle dynamics and multi-vehicle interaction， a Bayesian hierarchical model of real-time risk assessment was constructed based on the physical state layer， multi-vehicle interaction layer and risk probability layer of vehicles. The model parameters were calibrated by MCMC Gibbs sampling method. The model validity was tested by posterior prediction of p value and variation of parameter quantile. And the predictive performance of the model was evaluated by simulation analysis. The method of k-means clustering was adopted to classify the risk level of multi-vehicle interaction. The results show that the statistical BGR of model parameters are all less than 0.1. And the p value of Durbin-Watson's posterior， normal hypothesis， test distribution symmetry and kurtosis are all close to 0.5. So the risk assessment model is convergent and has good fitting. The AUC results show that the model is more accurate in identifying low risk， higher risk and highest risk， which has good performance. The research can evaluate the vehicle operation risk in a certain period of time and provide a reference for driving decision-making.

Key words: transportation safety engineering, real-time risk assessment, Bayesian hierarchical model, merging area, multi-vehicle interaction

CLC Number:

U492.8

Jie-yu ZHU,Yan-li MA. Real-time risk assessment method of multi-vehicle interaction at merging area[J].Journal of Jilin University(Engineering and Technology Edition), 2022, 52(7): 1574-1581.

Figures/Tables 14

Fig.1

Table 1

Fig.2

Fig.3

Table 2

Matrix construction"

	1	2	3	4	5	6	7	8
1	_	$S 12$	$S 13$	$S 14$	$S 15$	$S 16$	$S 17$	$S 18$
2	$S 21$	_	$S 23$	$S 24$	$S 25$	$S 26$	$S 27$	$S 28$
3	$S 31$	$S 32$	_	$S 34$	$S 35$	$S 36$	$S 37$	$S 38$
4	$S 41$	$S 42$	$S 43$	_	$S 45$	$S 46$	$S 47$	$S 48$
5	$S 51$	$S 52$	$S 53$	$S 54$	_	$S 56$	$S 57$	$S 58$
6	$S 61$	$S 62$	$S 63$	$S 64$	$S 65$	_	$S 67$	$S 68$
7	$S 71$	$S 72$	$S 73$	$S 74$	$S 75$	$S 76$	_	$S 78$
8	$S 81$	$S 82$	$S 83$	$S 84$	$S 85$	$S 86$	$S 87$	_

Table 2

Table 3

Parameter calibration of multi-vehicle interaction model"

交互车辆数/辆	$β 1$ / $10 - 2$	$β 2$ / $10 - 2$	$β 3$ / $10 - 3$	$β 4$ / $10 - 3$	$τ$
2	0.232	1.037	1.064	4.950	3.914
3	0.130	0.107	0.207	4.977	3.711
4	1.022	0.647	7.976	2.387	3.612
5	1.022	0.647	7.976	2.387	3.411
6	1.022	0.472	7.976	2.387	3.209
7	1.295	0.107	0.207	4.977	3.108

Table 3

Fig.4

Table 4

Fig.5

Table 5

Table 6

Table 7

Fig.6

Table 8

References 19

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t/s	x/m	y/m	v/（km·h^-1）	a/（m·s^-2）	θ/（°）
258.600	689.331	-68.385	75.838	3.462	-5.7
258.667	690.336	-66.073	66.279	6.792	-5.5
258.733	691.214	-63.897	63.866	3.627	-5.3
258.800	692.009	-62.313	63.117	2.000	-5.1
258.867	693.339	-60.087	62.482	3.390	-5.0
258.933	693.918	-58.232	64.088	8.913	-4.8
…	…	…	…	…	…
259.667	702.272	-36.304	63.419	2.971	-3.0
259.733	703.415	-34.323	61.016	4.147	-2.8

假设	统计量	p值
误差独立性	Durbin‐Watson	0.683
正态性	基于x²检验	0.372
方差齐性	Levene	0.401
偏度	偏度系数	0.418
峰度	峰度系数	0.537

车辆编号	车道	位置/m	速度 /（km·h^-1）	加速度 /（m·s^-2）	方向角 /（°）
…	…	…	…	…	…
26	1?1	554.596	65.41	1.79	163.85
27	4?1	445.012	54.32	0.19	163.86
31	5?1	390.686	60.06	0.09	162.76
33	4?1	337.727	57.12	-0.21	163.74
34	4?1	315.543	58.45	-0.20	163.56
36	1?1	338.451	58.14	-0.02	163.83
37	1?1	311.347	61.54	-0.07	163.79
…	…	…	…	…	…

风险级别	AUC	标准误差	渐近概率	95% LCL	95% UCL
低	0.716 95	0.051 31	1.869 76E-4	0.616 39	0.817 51
一般	0.684 68	0.055 30	0.004 71	0.576 29	0.793 06
较高	0.771 43	0.048 97	5.590 11E-6	0.675 46	0.867 41
高	0.803 34	0.045 98	1.876 27E-7	0.713 22	0.893 46

Real-time risk assessment method of multi-vehicle interaction at merging area

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