High-risk area identification method of expressway based on risk field

doi:10.16265/j.cnki.issn1003-3033.2023.05.1068

Abstract

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

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.

Figures/Tables 10

Tab.1

Fig.1

Fig.2

Tab.2

Tab.3

Field source risk in different structures area

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

Tab.3

Tab.4

Accident rate statistics of different types of road sections

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

Tab.4

Fig.3

Fig.4

Tab.5

Regional risk assessment level

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, +∞)

Tab.5

Tab.6

Results and comparison of predicted risk levels in various regions

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

Tab.6

References 25

[1]	International Traffic Safety Data and Analysis Group. Road safety annual report 2019[R], 2019.
[2]	李振明, 牛毅, 樊运晓, 等. 不同区域高速公路货车事故特征研究[J]. 中国安全科学学报, 2020, 30(6):121-127. doi: 10.16265/j.cnki.issn1003-3033.2020.06.018
	LI Zhenming, NIU Yi, FAN Yunxiao, et al. Study on characteristics of truck accidents in different regions of expressways[J]. China Safety Science Journal, 20, 30(6):121-127.
[3]	TSOURVELOUDIS N C, VALAVANIS K P, HEBERT T. Autonomous vehicle navigation utilizing electrostatic potential fields and fuzzy logic[J]. IEEE Transactions on Robotics and Automation, 2001, 17(4): 490-497. doi: 10.1109/70.954761
[4]	李晓弘. 六轮独立驱动电动车辆直驶驱动转矩分配策略研究[D]. 北京: 北京理工大学, 2015.
	LI Xiaohong. The Research of torque distribute strategy for 6-wheel-independent-driving electric vehicle[D]. Beijing: Beijing Institute of Technology, 2015.
[5]	BYRNE S, NAEEM W, FERGUSON S. Improved APF strategies for dual-arm local motion planning[J]. Transactions of the Institute of Measurement & Control, 2015, 37(1):73-90.
[6]	NI Daiheng. A unified perspective on traffic flow theory, part I: the field theory[J]. Applied Mathematical Sciences, 2013, 7:1929-1946. doi: 10.12988/ams.2013.13175
[7]	WOLF M T, BURDICK J W. Artificial potential functions for highway driving with collision avoidance[C]. 2008 IEEE International Conference on Robotics and Automation. IEEE, 2008: 3731-3736.
[8]	WANG Jianqiang, WU Jian, ZHENG Xunjia, et al. Driving safety field theory modeling and its application in pre-collision warning system[J]. Transportation research part C: Emerging Technologies, 2016, 72: 306-324. doi: 10.1016/j.trc.2016.10.003
[9]	HU Hongyu, ZHANG Chi, SHENG Yuhan, et al. An improved artificial potential field model considering vehicle velocity for autonomous driving[J]. IFAC-PapersOnLine, 2018, 51(31): 863-867. doi: 10.1016/j.ifacol.2018.10.095
[10]	陶鹏飞, 金盛, 王殿海. 基于人工势能场的跟驰模型[J]. 东南大学学报:自然科学版, 2011, 41(4): 854-858.
	TAO Pengfei, JIN Sheng, WANG Dianhai. Car following model based on artificial potential energy field[J]. Journal of Southeast University:Natural Science Edition, 2011, 41 (4): 854-858.
[11]	ORFILA O, COIRET A, DO M T, et al. Modeling of dynamic vehicle-road interactions for safety-related road evaluation[J]. Accident Analysis & Prevention, 2010, 42(6): 1736-1743. doi: 10.1016/j.aap.2010.04.014
[12]	尚婷, 吴鹏, 唐伯明, 等. 隧道至互通小间距路段车辆换道博弈行为研究[J]. 中国安全科学学报, 2021, 31(10): 68-75. doi: 10.16265/j.cnki.issn1003-3033.2021.10.010
	SHANG Ting, WU Peng, TANG Boming, et al. Study on lane-changing game behavior of vehicles in small spacing section between tunnel and interchange[J]. China Safety Science Journal, 2021, 31(10): 68-75. doi: 10.16265/j.cnki.issn1003-3033.2021.10.010
[13]	吴剑. 考虑人—车—路因素的行车风险评价方法研究[D]. 北京: 清华大学, 2015.
	WU Jian. Research on driver-vehicle-road factors considered driving risk evaluation method[D]. Beijing: Tsinghua University, 2015.
[14]	谢树艺. 工程数学:矢量分析与场论 :第2版[M]. 北京: 高等教育出版社, 1984:27-48.
[15]	SOLOMON D. Accidents on main rural highways related to speed, driver, and vehicle[R]. Bureau of Public Roads, 1964.
[16]	郭应时, 付锐, 袁伟, 等. 山区公路事故率与平面线形的关系[J]. 交通运输工程学报, 2012, 12(1):63-71.
	GUO Yingshi, FU Rui, YUAN Wei, et al. Relationship between highway accident rate and plane alignment in mountainous areas[J]. Journal of Traffic and Transportation Engineering, 2012, 12 (1): 63-71.
[17]	符锌砂. 理论运行速度与公路线形设计及评价方法研究[D]. 西安: 长安大学, 2008.
	FU Xinsha. Research on theoretical operation velocity versus highway alignment design and their evaluation methods[D]. Xi'an: Chang'an University, 2008.
[18]	陆建, 孙祥龙, 戴越. 普通公路车速分布特性的回归分析[J]. 东南大学学报:自然科学版, 2012, 42(2): 374-377.
	LU Jian, SUN Xianglong, DAI Yue. Regression analysis of speed distribution characteristics of ordinary highway[J] Journal of Southeast University:Natural Science Edition, 2012, 42 (2): 374-377.
[19]	曲大义, 郝亮, 陈秀锋, 等. 车流速度离散对运行安全的影响研究[J]. 青岛理工大学学报, 2012, 33(6):1-5.
	QU Dayi, HAO Liang, CHEN Xiufeng, et al. Study on the impact of vehicle flow velocity dispersion on operation safety[J]. Journal of Qingdao University of Technology, 2012, 33 (6): 1-5.
[20]	徐进, 陈莹, 陈海源, 等. 回头曲线路段的轨迹行为模式与事故风险[J]. 东南大学学报:自然科学版, 2020, 50(5):973-982.
	XU Jin, CHEN Ying, CHEN Haiyuan, et al. Vehicle track patterns and accident risk on hairpin curves[J]. Journal of Southeast University:Natural Science Edition, 2020, 50(5):973-982.
[21]	XU Jin, LUO Xiao, SHAO Yi Ming. Vehicle trajectory at curved sections of two-lane mountain roads: a field study under natural driving conditions[J]. European Transport Research Review, 2018, 10: 1-16. doi: 10.1007/s12544-017-0273-5
[22]	REN Yuanyuan, LI Xiansheng, REN You. Study on driving dangerous area in road curved section based on vehicle track characteristics[J]. International Journal of Computational Intelligence Systems, 2011, 4(6): 1237-1245.
[23]	孟祥海, 荆林朋, 侯芹忠. 高速公路平纵线形与事故率的关系及其安全性评价[J]. 交通信息与安全, 2015, 33(4):69-75,126.
	MENG Xianghai, JING Linpeng, HOU Qinzhong. Relationship between horizontal and vertical alignment of Expressway and accident rate and its safety evaluation[J]. Journal of Traffic Information and Safety, 2015, 33 (4): 69-75,126.
[24]	JTG B05—2015, 公路项目安全性评价规范[S].
	JTG B05-2015, Specification for safety evaluation of highway projects[S].
[25]	张驰, 胡涛, 侯宇迪, 等. 基于制动毂温升的连续下坡货车事故风险评价模型[J]. 华南理工大学学报:自然科学版, 2020, 48(11):19-29.
	ZHANG Chi, HU Tao, HOU Yudi, et al. Accident risk assessment model of continuous downhill freight cars based on brake hub temperature rise[J]. Journal of South China University of Technology:Natural Science Edition, 2020, 48(11): 19-29.

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