南京林业大学学报(自然科学版) ›› 2010, Vol. 34 ›› Issue (06): 157-160.doi: 10.3969/j.jssn.1000-2006.2010.06.034

• 研究简报 • 上一篇    下一篇

道路交通事故黑点成因鉴别与改善对策

林丽,张永强,高敏杰   

  1. 南京林业大学土木工程学院,江苏南京210037
  • 出版日期:2010-12-27 发布日期:2010-12-27
  • 基金资助:
    收稿日期:2009-10-15修回日期:2010-05-10作者简介:林丽(1974—),副教授。Email: linli401@nifu.com.cn。引文格式:林丽,张永强,高敏杰.

Formation cause identification and improvement of the road traffic accidents black spots

LIN Li, ZHANG Yongqiang, GAO Minjie   

  1. College of Civil Engineering, Nanjing Forestry University, Nanjing 210037, China
  • Online:2010-12-27 Published:2010-12-27

摘要: 为提高道路交通安全性,消除事故隐患,研究了道路、交通、交通设施和环境等方面对道路交通安全的影响,在分析交通事故黑点的成因鉴别方法(包括模糊聚类法、灰色关 联分析、粗集理论)及其适应性基础上,以国道G104线K2177—K2178段为例,对公路交通中的不利因素进行筛选,发现公路事故黑点的主要因素是国道周边环境干扰,次要因素是未 设置道路照明设施。3种交通事故黑点成因鉴别方法比较结果表明:模糊聚类法适用于处理大数据的事故黑点,灰色关联分析法适用于处理黑点成因主因素已确定的事故黑点,粗集 理论可用于鉴别事故资料不完善的交通事故黑点的成因。

Abstract: Effects of road, traffic, facilities and environment on road traffic safety were investigated for improving road safety and eliminating traffic black spots. On the basis of analysis cause identification methods about black spots, including fuzzy clustering, gray correlation analysis, and rough set, and its suitability, taking G104 line K2177—K2178 section for example, and screening disadvantages in road traffic, it was found that the main factor of black spot was the interfering of surrounding environment and the secondary factor is the lack of lighting facilities, and traffic infrastructures were taken to improve it. The results showed that: Fuzzy clustering was applied to handle black spots which had large accidents data, and gray correlation analysis method could be applied to deal with black spots of which the major factors had been determined, and rough set could be used to identify the causes of black spots with imperfect information.

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