基于用户移动行为相似性聚类的Markov位置预测

由于采集点丢失或出现新用户等原因,GPS轨迹数据往往具有稀疏性,使得基于单个用户数据的位置预测准确率较低.针对这种情况,提出了基于移动行为相似性和用户聚类的Markov位置预测方法.首先,基于Voronoi图和原始GPS轨迹进行区域划分,位置预测基于区域轨迹进行;其次,提出了同时考虑用户转移特性和用户区域特性的移动行为相似性计算方法;再次,根据移动行为相似性对用户进行聚类,并在聚类的用户组上采用一阶Markov模型进行位置预测,提高了位置预测的准确性.真实GPS轨迹数据上的实验表明了所提出方法的有效性.

Markov Location Prediction Based on User Mobile Behavior Similarity Clustering

GPS trajectories are often sparse due to the sampling points lost or new users appearing， which makes the accuracy of location prediction low based on the data of a single user. To solve this problem， a novel Markov location prediction approach was proposed based on user mobile behavior similarity and user clustering. First， the map was partitioned into various regions based on Voronoi diagram and original GPS trajectories. And then locations were predicted over region trajectories. Second， a new approach was proposed to measure the similarity of users’ mobile behaviors by considering users’ transferring features and regional features. Third，based on the mobile behavior similarity， users were divided into various groups and the first-order Markov model was applied on the groups to predict users’ locations. Therefore， the accuracy of location prediction was improved. The experiments over real GPS trajectory dataset indicate that the proposed method is effective for location prediction.