基于风险场的高速公路高风险区域甄别方法

doi:10.16265/j.cnki.issn1003-3033.2023.05.1068

摘要/Abstract

摘要：

为有效识别高速公路高风险区域,首先,在理论阐述和量化解释行车风险演化机制基础上引入势场理论,提出道路静态风险场的基本概念及性质;然后,在分析道路要素对行车安全影响的基础上,构建路域范围内的构造物、线形、路侧等要素的静态风险场计算模型,提出高速公路高风险区域甄别方法,同时,基于交通事故统计数据,对静态风险场计算模型的风险量进行参数标定;最后,依托实际项目,进行区域风险等级预测和有效性验证。结果表明:研究路段的33处区域中26处甄别结果与实际风险等级相同,仅有7处结果不一致,风险等级结果相差一个等级内的区域数量为30处; 风险等级预测结果准确率达78.79%,与实际风险等级结果相差一个等级内的准确率达90.91%。静态风险场能够有效应用于高风险区域甄别,研究有助于设计及运营阶段高速公路安全治理。

关键词: 风险场, 高速公路, 高风险区域甄别, 行车风险, 道路静态风险场

Abstract:

In order to effectively identify high risk areas of expressways, firstly, the potential field theory was introduced on the basis of theoretical exposition and quantitative interpretation of driving risk evolution mechanism, and the basic concept and properties of road static risk field were proposed. Then, on the basis of analyzing the influence of road factors on traffic safety, the static risk field calculation model of structures, alignments, roadsides and other factors in the road domain was constructed, and the method of identifying high risk areas of expressways was proposed. At the same time, based on the statistical data of traffic accidents, the risk quantity parameters of the static risk field calculation model were calibrated. Finally, based on the actual project, the regional risk level prediction and effectiveness verification were carried out. The results show that among the 33 areas of the study section, 26 of the screening results are the same as the actual risk level, only 7 of the results are inconsistent, and the number of areas within one level of risk level difference is 30. The accuracy of the risk level prediction results is 78.79%, and the accuracy of the actual risk level results is 90.91%. The static risk field can be effectively applied to the identification of high-risk areas, and the research is helpful to the highway safety management in the design and operation stages.

Key words: risk field, expressway, high-risk areas identification, driving risk, road static risk field

张驰, 王博, 陈星光, 任士鹏, 翟艺阳. 基于风险场的高速公路高风险区域甄别方法[J]. 中国安全科学学报, 2023, 33(5): 144-151.

ZHANG Chi, WANG Bo, CHEN Xingguang, REN Shipeng, ZHAI Yiyang. High-risk area identification method of expressway based on risk field[J]. China Safety Science Journal, 2023, 33(5): 144-151.

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护栏类型	事故起数	伤亡人数	N_Li/ (人·次^-1)	Q_Li
柔性护栏	32	5	0.16	1.00
半刚性护栏	324	61	0.19	1.19
刚性护栏	438	112	0.26	1.63

构造物类型	样本量	全年交通量/veh	路段长度/km	事故起数	事故率/ (次·百万车^-1·km^-1)	$Q g i$
一般路段	—	3 617 539	12.69	11	0.239	1.0
隧道	28		71.07	88	0.342	1.5
互通	12		15.56	45	0.799	3.3
桥梁	71		26.00	62	0.659	2.8

类型编号	路段类型	样本数量	全年交通量/ veh	路段长度/ km	事故数	事故率/ (次·亿车^-1·km^-1)	$Q L i$
1	直线-小纵坡	42	3 617 539	63.95	49	20.75	1.00
2	直线-大上坡	46		13.87	11	21.92	1.06
3	直线-大下坡	132		13.87	11	21.92	1.06
4	小半径曲线-小纵坡	54		48.05	44	25.31	1.22
5	小半径曲线-大上坡	78		18.43	18	27.00	1.30
6	小半径曲线-大下坡	128		18.43	19	28.50	1.37
7	大半径曲线-小纵坡	12		48.17	40	22.95	1.11
8	大半径曲线-大上坡	18		8.15	7	23.74	1.14
9	大半径曲线-大下坡	32		8.15	7	23.74	1.14

风险等级	极低风险	低风险	较低风险	中风险	较高风险	高风险
$E ¯$	(0,80)	[80, 90)	[90, 100)	[100, 110)	[110, 120)	[120, +∞)
CR	(0,0.3)	(0.3, 0.6]	(0.6, 0.975]	(0.975, 1.2]	(1.2, 1.5)	[1.5, +∞)

起点桩号	终点桩号	$E ¯$	预测风险等级	2014—2020年路段事故总数	CR	实际风险等级
K130+010	K131+010	100.635	中	25	0.983	中
K131+010	K132+010	117.849	较高	33	1.297	较高
K132+010	K133+010	137.738	高	45	1.769	高
K133+010	K134+010	82.502	低	12	0.472	低
K134+010	K135+010	95.443	较低	22	0.865	较低
K135+010	K136+010	114.621	较高	21	0.826	较低
K136+010	K137+010	102.660	中	29	1.140	中
K137+010	K138+010	90.991	较低	18	0.708	较低
K138+010	K139+010	95.180	较低	16	0.629	较低
K139+010	K140+010	104.233	中	25	0.983	中
K140+010	K141+010	125.369	高	33	1.297	较高
K141+010	K142+010	103.174	中	30	1.179	中
K142+010	K143+010	85.923	低	17	0.668	较低
K143+010	K144+010	93.216	较低	19	0.747	较低
K144+010	K145+010	100.022	中	24	0.943	较低
K145+010	K146+010	96.046	较低	20	0.786	较低
K146+010	K147+010	95.595	较低	16	0.629	较低
K147+010	K148+010	85.235	低	9	0.354	低
K148+010	K149+010	89.187	低	11	0.432	低
K149+010	K150+010	100.247	中	14	0.550	低
K150+010	K151+010	87.608	低	9	0.354	低
K151+010	K152+010	122.273	高	42	1.651	高
K152+010	K153+010	78.878	极低	2	0.079	极低
K153+010	K154+010	93.636	较低	3	0.118	极低
K154+010	K155+010	80.900	低	8	0.314	低
K155+010	K156+010	97.487	较低	19	0.747	较低
K156+010	K157+010	85.560	低	13	0.511	低
K157+010	K158+010	103.841	中	30	1.179	中
K158+010	K159+010	122.419	高	39	1.533	高
K159+010	K160+010	107.732	中	26	1.022	中
K160+010	K161+010	77.970	极低	2	0.079	极低
K161+010	K162+010	75.863	极低	2	0.079	极低
K162+010	K162+410	112.955	较高	28	1.101	中