基于数据降维与聚类的车联网数据分析应用

Application of Data Analysis of Internet of Vehicles Based on Data Dimension Reduction and Clustering

智能网联背景下的车联网数据分析与应用对提升交通智能化有重要影响。为了加快交通智能化进程,文章对车联网数据应用过程中存在的数据冗余问题进行研究。以采集的车联网数据为研究对象,驾驶行为特点分类辨识为研究目标。采用相关分析与主成分分析方法对数据进行冗余筛选与降维,使用k-means聚类算法对驾驶行为特点进行分类辨识。研究结果表明,使用数据降维的方法可以降低车联网数据的相关冗余性,驾驶行为特点分类辨识结果表明其特点可分为三类驾驶行为。研究提升了车联网数据的应用价值,也为交通智能化提供了相关的支持。

The data analysis and application of Internet of vehicles under the background of intelligent network has an important impact on improving traffic intelligence.In order to speed up the process of traffic intelligent,data redundancy in the data application of Internet of vehicles is studied.The data collected from the Internet of vehicles is taken as the research object,and the classification and identification of driving behavior characteristics is taken as the research object.Correlation analysis and principal component analysis were used for redundancy screening and dimensionality reduction of data,and k-means clustering algorithm was used for classification and identification of driving behavior characteristics.The results show that the data dimension reduction method can reduce the correlation redundancy of the Internet of vehicles data,and the characteristics of driving behaviors can be classified into three types of driving behaviors.The research improves the application value of the data of the Internet of vehicles,and also provides relevant support for traffic intelligence.