基于K-Means聚类与灰色关联分析的城市交通状况分析

Analytical study of urban traffic conditions based on K-Means clustering and grey correlation analysis

为了分析交通拥堵的影响因素,将数据挖掘技术用于城市拥堵成因分析中,提出一种基于聚类与灰色系统理论的城市交通状况分析方法。构建包括交通拥堵状况、交通基础设施、城市国民经济与社会发展的城市交通状况分析指标体系,利用K-Means聚类法对全国36个重点城市的交通状况进行等级划分,并将划分结果进行灰色关联分析,研究交通状况与交通基础设施建设、国民经济与社会发展水平之间的内在关联。研究表明:城市每千人民用汽车拥有量与建设区道路密度是影响交通拥堵状况的主导因素,每万人公共汽车拥有量、从业人口密度以及常住人口密度为主要间接影响因素。

To analyze the factors affecting traffic congestion,the data mining technology is used in the analysis of the causes of urban congestion and a method of urban traffic condition analysis based on clustering and grey system theory is proposed.From the aspects of urban traffic congestion,traffic infrastructure,and the city′s national economy and social development,this paper establishes an analysis index system of urban traffic conditions.Then,the K-means clustering method is used to divide the traffic conditions of 36 key cities in China into levels.As a result,the clustering results are used in the grey correlation analysis to study the traffic congestion,infrastructure constructionand the internal relationship between the national economy and social development level.The research shows that the car ownership per thousand people and the road density in the construction area are the main factors affecting the traffic congestion,while the ownership of public vehicles per ten thousand people,the density of employed population and the density of permanent residents are the main indirect factors.