基于DENCLUE聚类算法的交通事故多发点鉴别方法

针对目前交通事故多发点鉴别常用方法存在的问题，引入了DENCLUE聚类算法用于事故多发点鉴别。对DENCLUE聚类算法的基本原理，基本定义及计算步骤进行了阐述，重点分析了该算法用于事故多发点鉴别的可行性。实例计算结果表明：与传统事故多发点鉴别法方法相比，该算法能有效的避免对排查位置进行事先划分，实现任意长度聚类；同时，在事故数据小样本的情况下，能充分凸显道路沿线的危险性，可以有效地应用于事故多发点鉴别的研究。

Accident-prone Location Identification Method Based on DENCLUE Clustering Algorithm

Due to the existing problems associated with current accident-prone location identification methods, DENCLUE clustering algorithm was introduced to fulfill a more effective and efficient identification purpose. This paper explicated the DENCLUE algorithm about its basic rationale, primary definition, calculation process, with specially emphasizing the application to field challenges. A demonstration example in the field context was calculated with the proposed algorithm. It suggests that the algorithm can effectively aw）id a pre-division of the identified locations and realize a cluster generation with random shape, comparing with those traditional methods. Furthermore, it possesses a merit to fully reflect the road risk even with small incident sample size, and thus can be applied to the research on accident-prone location identification issue.