Kernel density estimation and K-means clustering to profile road accident hotspots
Kernel density estimation and K-means clustering to profile road accident hotspots
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DOI：
10.1016/j.aap.2008.12.014
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发表时间：
2009-05-01
影响因子：
5.9
通讯作者：
Anderson, Tessa K.
中科院分区：
文献类型：
--
作者：
Anderson, Tessa K.
确定道路事故热点是确定减少事故高密度地区的有效战略的关键作用。本文介绍了（1）使用地理信息系统（GIS）和核密度估计的方法来研究的空间格局的伤害相关的道路交通事故在伦敦，英国和（2）聚类方法使用环境数据和结果的第一部分，以创建一个分类的道路交通事故热点。这种方法的使用将说明使用伦敦地区在英国。使用了伦敦警察厅1999年至2003年收集的道路事故数据。核密度估计地图创建，随后分解细胞密度，以创建一个基本的空间单元的事故热点。然后将收集的环境数据添加到热点单元格中，并使用K均值聚类，破译类似热点的结果。基于碰撞和属性数据创建了5个组和15个聚类。这些集群进行了讨论和评估，根据其鲁棒性和潜在用途的道路安全运动。(C)2008爱思唯尔有限公司保留所有权利。
Identifying road accident hotspots is a key role in determining effective strategies for the reduction of high density areas of accidents. This paper presents (1) a methodology using Geographical Information Systems (GIS) and Kernel Density Estimation to study the spatial patterns of injury related road accidents in London, UK and (2) a clustering methodology using environmental data and results from the first section in order to create a classification of road accident hotspots. The use of this methodology will be illustrated using the London area in the UK. Road accident data collected by the Metropolitan Police from 1999 to 2003 was used. A kernel density estimation map was created and subsequently disaggregated by cell density to create a basic spatial unit of an accident hotspot. Appended environmental data was then added to the hotspot cells and using K-means clustering, an outcome of similar hotspots was deciphered. Five groups and 15 clusters were created based on collision and attribute data. These clusters are discussed and evaluated according to their robustness and potential uses in road safety campaigning. (C) 2008 Elsevier Ltd. All rights reserved.