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

作　　者：潘立[1,2] 邓佳[1] 王永利[1] 

机构地区：[1]南京理工大学计算机科学与工程学院,南京210094 [2]中国人民解放军火箭军参谋部,北京100085

出　　处：《计算机工程与应用》2018年第1期77-85,共9页Computer Engineering and Applications

基　　金：国家自然科学基金(No.61170035);"江苏省六大人才高峰"高层次人才项目(No.WLW-004);中央高校基本科研业务费专项资金(No.30916011328);江苏省科技成果转化专项资金(No.BA2013047)

摘　　要：随着经济的发展,城市交通拥堵问题亟待解决,交通量过载发现是解决交通拥堵问题的有效方法之一。提出一种基于HMM模型的轨迹聚类算法HMM-Cluster,可有效地发现交通量过载情况。该算法首先提取时空轨迹特征点,并采用维数约简技术减少轨迹数据量,根据参照轨迹拟合HMM模型,基于密度函数得到轨迹相似度矩阵,最后给出聚合的相似性轨迹。真实轨迹数据集上的对比实验结果表明,提出的HMM-Cluster可有效地挖掘移动对象运动模式,准确发现交通量过载情况,具有一定实用价值。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 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论]