基于时间序列聚类的轨迹停留点检测算法

Trajectory stops detection algorithm based on time series clustering

针对采样不规则轨迹的停留点检测准确性不高的问题,提出了一种基于时间序列聚类的停留点检测算法。首先基于数据场理论设计了一种综合考虑时空特性的混合特征密度测量方法,然后根据停留点中心密度比入口大的特性,采用过滤—精炼策略提取停留点。在过滤阶段,将时间连续且满足最小密度阈值的点作为候选停留点。在精炼阶段,通过最大阈值筛选出实际停留点。实验结果表明,该方法能够有效检测采样不规则轨迹中的停留点,相较于已有方法具有较高的准确性和较低的时间消耗。

According to the shortcoming that the low accuracy during detect the stops of sampling irregular trajectory,this paper proposed an algorithm based on the time series clustering to find the stops in trajectories.Firstly,based on the data field theory,this work designed a hybrid feature density measurement method considering the spatial and temporal characteristics.Then,this method used the filtering-refining strategy to extract stops based on the feature that the center of stop was denser than the entrance.In the filtering phase,some points that were continuous in time and met the minimum density threshold was taken as back up stops.During the refining phase,it filtered true stops according to the maximum threshold.The experimental results show that the proposed method can effectively detect the staying points in the sampling irregular trajectory.It not only has lower time consumption than the existing methods,also has higher accuracy.