Research on identification of black spots for highway based on system clustering method

doi:10.16265/j.cnki.issn1003-3033.2018.11.017

Abstract

Abstract: In order to improve the road traffic environment in the plateau area,and increase the identification accuracy of black spots, based on statistical data on road traffic accidents in Yunnan province from 2010 to 2015, the statistical analysis method was used to analyze the characteristics of highway traffic accidents and temporal and spatial distribution in the plateau area. Regarding the number of accidents and the number of equivalent accidents in millions of kilometers as indicators, the systematic clustering method was used to identify the type of highway traffic accidents in the plateau area. Then, it was analyzed with Kunshi expressway as an example,a total of 11 accident-prone points were identified, which match the actual situation effectively. The results show that,in terms of time distribution,highway traffic accidents in the plateau area happen frequently in winter and spring, that the accident rate is highest in the afternoon and the evening, that in terms of space, road accidents in the counties and townships in the plateau region occur more frequently, that from the point of view of accidents, highway accidents in the plateau area are mostly frontal collisions and scratching pedestrians, which occupied 57% of the total number of accidents.

Key words: traffic engineering, plateau area, highway traffic, system clustering, accident multiple point, identification

CLC Number:

X928.03

NIU Zhipeng, QI Shouming, WU Dongling, LIU Wenjia, LIAN Guan. Research on identification of black spots for highway based on system clustering method[J]. China Safety Science Journal, 2018, 28(11): 104-109.

References

[1] RUNE E. A survey of operational definitions of hazardous road locations in some European countries[J].Accident Analysis and Prevention, 2008, 40(6): 1 830-1 835.
[2] AHMED M, HUANG H, ABDEL-ATY M, et al. Exploring a Bayesian hierarchical approach for developing safety performance functions for a mountainous freeway [J]. Accident Analysis and Prevention, 2011, 43(4): 1 581-1 589.
[3] SENK P, AMBROS J, POKORNY P, et al. Use of accident prediction models in identifying hazardous road locations [J]. Transactions on Transport Sciences, 2012, 5(5): 223-232.
[4] ANA F, JOSE N. An approach to accidents modeling based on compounds road environments[J].Accident Analysis and Prevention, 2013, 53: 39-45.
[5] 裴玉龙. 道路交通事故多发点质量控制鉴别法的改进[J]. 哈尔滨工业大学学报, 2006, 38(1): 97-100.
PEI Yulong. Improvement in the quality control method to distinguish the blackspot of the road [J]. Journal of Harbin Institute of Technology, 2006, 38(1): 97-100.
[6] 李亭. 山区高等级公路黑点段鉴别及安全性评价研究[D]. 长春: 吉林大学, 2008.
LI Ting. Research on the black-spots identification and safety evaluation of high-level highway in mountains [D]. Changchun: Jilin University, 2008.
[7] 袁泉, 李一兵, 鲁光泉. 基于地理视角的交通事故多发点鉴别分析模型[J]. 交通运输工程学报, 2010, 10(1): 101-105.
YUAN Quan, LI Yibing, LU Guangquan. Identification analysis model of traffic accident-prone locations based on geographical view angle [J]. Journal of Traffic and Transportation Engineering, 2010, 10(1): 101-105.
[8] 孙连超, 邵毅明, 颜雪丽,等. 基于TCT的公路交通事故黑点鉴别方法研究[J]. 重庆交通大学学报:自然科学版, 2012, 31(1): 63-67.
SUN Lianchao, SHAO Yiming, YAN Xueli, et,al. Identification of highway traffic accident black-spots based on traffic conflict technique [J]. Journal of Chongqing Jiaotong University:Natural Science, 2012, 31(1): 63-67.
[9] 李觅佳. 基于不良天气因素影响下山区高速公路事故多发点鉴别[D]. 西安: 长安大学, 2014.
LI Mijia. Research on the identification method of accident-prone section of freeway in the mountainous area based on the affecting factors of bad weather [D]. Xi'an: Chang'an University, 2014.
[10] 孟祥海, 覃薇. 基于统计及假设检验的高速公路事故多发点分析[J]. 中国安全科学学报, 2017, 27(5): 158-163.
MENG Xianghai, QIN Wei. Analysis of black spot for freeway based on both statistics and hypothesis testing [J]. China Safety Science Journal, 2017, 27(5): 158-163.

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