基于K-means聚类算法的公交行程速度计算模型  

Computational Model of Average Travel Speed Based on K-means Algorithms

作　　者：高曼[1] 韩勇[1] 陈戈[1] 张小垒[1] 李洁[1] 

机构地区：[1]中国海洋大学信息科学与工程学院,青岛266100

出　　处：《计算机科学》2016年第S1期422-424 439,共4页Computer Science

基　　金：青岛市科技发展计划(13-1-3-117-nsh)资助

摘　　要：定位与无线装置在公交系统中的广泛应用使得获取实时公交数据成为可能。为挖掘这些数据中蕴含的道路交通状况信息,提出了一种基于K-means聚类算法的数据融合模型,来计算相邻公交站点间的平均行程速度。首先对K-means聚类算法进行改进:(1)聚类数K不是预先设定的固定值,而是不重复样本数的平方根,不同路段不同时段K值不同;(2)初始聚类中心不是随机选取,而是根据K值按一定规则选取。其次利用改进的算法对样本数据进行聚类,然后对各类数据进行加权融合,计算出平均行程速度。最后通过折线图对青岛市4个城区的行程速度进行分析,挖掘交通流的演变规律。研究结果为交通管理、居民出行等提供了强有力的支持。It is possible to retrieve real-time data using floating bus data acquisition system equipped with positioning and wireless communication apparatus.To explore traffic condition,a data fusion model based on the K-means clustering algorithm was put forward.The model was used to calculate the average travel speed between adjacent bus stops.At first,K-means clustering algorithm was improved:(1)the cluster number Kis not predefined but the square root of nonidentical sample size,and it is different at different sections and time;(2)the initial cluster center is not random but selected according to K.Then,the sample data were divided into K classes by the improved algorithm and the average travel speed was obtained by data fusion model.Finally,the average travel speed of four areas in Qingdao was shown by line charts to explore some evolution law of traffic flow.The research provides strong support for traffic management and residents travel.

关 键 词：公共交通 平均行程速度 K-均值聚类算法 数据融合 数据挖掘 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论]