计算机工程与应用2018，Vol.54Issue(1)：77-85,9.DOI:10.3778/j.issn.1002-8331.1612-0528
HMM-Cluster:面向交通量过载发现的轨迹聚类方法
HMM-Cluster:Trajectory clustering for discovering traffic volume overload
摘要
Abstract
With the development of economy, the urban traffic congestion has become an urgent problem in China. The traffic volume overload discovering is an effective method for solving the problem of traffic congestion. A kind of trajectory clustering method based on the HMM model, named HMM-Cluster, is put forward, which can find out the traffic volume overload conditions. HMM-Cluster extracts the feature points of spatio-temporal trajectory data firstly, and it uses dimension reduction technique to decrease the trajectory data volume, as well as save the cost of storage. Secondly, it trains a HMM model for each reference trajectory based on density function to get a trajectory affinity similarity matrix. Finally, the HMM-Cluster algorithm aggregates similarity trajectory effectively and forms the clustering results of trajectory data. The contrast experiments on actual data prove that the HMM-Cluster method has a good effect, which can obtain moving objects'pattern and discover traffic volume overload effectively and conveniently. The proposed method has significant values in real application.关键词
交通量过载/时空数据/轨迹聚类/隐马尔科夫模型Key words
traffic volume overload/spatio-temporal data/trajectory clustering/Hidden Markov Model(HMM)分类
信息技术与安全科学引用本文复制引用
潘立,邓佳,王永利..HMM-Cluster:面向交通量过载发现的轨迹聚类方法[J].计算机工程与应用,2018,54(1):77-85,9.基金项目
国家自然科学基金(No.61170035) （No.61170035）
"江苏省六大人才高峰"高层次人才项目(No.WLW-004) （No.WLW-004）
中央高校基本科研业务费专项资金(No.30916011328) （No.30916011328）
江苏省科技成果转化专项资金(No.BA2013047). （No.BA2013047）
