基于改进聚类算法的交通事故多发点识别方法

道路交通事故多发点事故发生频率高且严重性突出，为提高道路通行的安全与效率，需要找到事故多发点所在位置。针对现有密度聚类算法对交通事故多发点识别时需要设置中心点个数以及容易扩大聚类范围等问题，提出一种限制簇扩展的自适应搜索密度峰值聚类算法(limit cluster expansion and adaptive search clustering by fast search and find of density peaks, LA-CFDP)。LA-CFDP算法通过增加中心点限制条件自动确定中心点个数，引入参数扩展因子限制簇扩展范围，从而提高算法对事故多发点识别的适应性和准确性。在英国四个城市2019年交通事故数据集上的实例分析表明，LA-CFDP算法对四个城市聚类结果的轮廓系数值达到0.72～0.92,DBI值均降低到0.37以下。聚类结果符合事故多发点的定义及特征，能够为交通事故多发点治理提供可靠依据。

Identifying method of traffic accident-prone spots based on improved clustering algorithm

Road traffic accidents occur frequently and seriously in the accident-prone spots. In order to improve the safety and efficiency of road traffic, it is necessary to find the location of accident-prone points. The existing density clustering algorithm needed to set the number of center points and was easy to expand the clustering range when identifying traffic accident-prone points, this paper proposed the limit cluster expansion and adaptive search clustering by fast search and find of density peaks(LA-CFDP) algorithm to solve these problems. LA-CFDP algorithm automatically determined the number of center points by increasing the restriction condition of center points, and introduced the parameter expansion factor to limit the cluster expansion range, so as to improve the adaptability and accuracy of the algorithm for accident-prone point identification. The case analysis on the 2019 traffic accident data set of four cities in United Kingdom shows that the Sihouette coefficient of the clustering results of LA-CFDP algorithm reaches 0.72~0.92, and the Davies-Bouldin index(DBI) are all reduced to below 0.37. The clustering results accord with the definition and characteristics of accident-prone spots, and can provide reliable basis for the management of accident-prone spots.