Based on the analysis on limitation of present common road accident-prone location identifica-tion method ,an improved DBSCAN clustering algorithm was proposed to identify the accident-prone location .In the algorithm ,the minimal threshold value of clustering is adaptively changed in combina-tion with cumulative frequency curve method ,where the optimal value can be found .The application in a highway of Anhui province shows that ,the improved DBSCAN clustering algorithm not only can identify the clustering of highway accident-prone area with any section length ,but also be able to make the section length more concentrate .In addition ,it will not miss any accident-prone section or expand the accident-prone area .It is proved to be an effective method to studying spatial distribution characteristics of accident-prone location .%针对现有道路事故多发段鉴别方法在阈值选择中存在的缺点和局限，提出了一种改进的DBSCAN聚类算法用于鉴别事故多发段。聚类算法中最小密度点的选取结合了累计频率曲线方法，能实现阈值的自适应选取。算法应用于安徽省某高速公路，结果表明采用的聚类算法能实现任意长度的聚类，不会遗漏事故多发段或扩大事故多发段范围，且识别的事故多发路段更为集中，是一种鉴别事故多发段空间分布特征的有效方法。
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