关键词: 城市交通, 交通安全, 地理编码, 密度聚类, 轮廓系数

Abstract:

Traffic accident characteristics are significantly affected by regional distribution. In this paper, traffic accident characteristics are clustered by the optimized density- based spatial clustering of applications with noise (DBSCAN) clustering method. The 2019 traffic accident data in Wuxi, China is used as a case study. The open map API is used to obtain the longitude and latitude of the accident location as an input for the proposed method. The traditional DBSCAN clustering algorithm normally requires accurate input of the distance threshold and sample number threshold. This paper develops the DBSCAN clustering model with an adaptive search distance threshold and sample number threshold. The comparison results of the proposed algorithm with traditional algorithm show that the optimized algorithm is more intelligent in determining parameters and more accurate in dividing clusters; the recognition of noise points is more reasonable than the traditional algorithm. The applicability of the algorithm in the geographical location clustering for urban road traffic accidents is proved by calculating the model score of the silhouette coefficient in machine learning.

Key words: urban traffic, traffic safety, geocoding, density clustering, silhouette coefficient