Trajectory Time Series Compression Algorithm Based on Unsupervised Segmentation  

作　　者：Shuang SUN Yan CHEN Zaiji PIAO 

机构地区：[1]School of Maritime Economics and Management,Dalian Maritime University,Dalian 116026,China [2]School of Software,Dalian University of Foreign Languages,Dalian 116044,China

出　　处：《Journal of Systems Science and Information》2024年第3期360-378,共19页系统科学与信息学报（英文）

基　　金：Supported by the Basic Research Projects of Liaoning Provincial Department of Education(LJKQZ20222459)。

摘　　要：Aiming at the problem of ignoring the importance of starting point features of trajecory segmentation in existing trajectory compression algorithms,a study was conducted on the preprocessing process of trajectory time series.Firstly,an algorithm improvement was proposed based on the segmentation algorithm GRASP-UTS(Greedy Randomized Adaptive Search Procedure for Unsupervised Trajectory Segmentation).On the basis of considering trajectory coverage,this algorithm designs an adaptive parameter adjustment to segment long-term trajectory data reasonably and the identification of an optimal starting point for segmentation.Then the compression efficiency of typical offline and online algorithms,such as the Douglas-Peucker algorithm,the Sliding Window algorithm and its enhancements,was compared before and after segmentation.The experimental findings highlight that the Adaptive Parameters GRASP-UTS segmentation approach leads to higher fitting precision in trajectory time series compression and improved algorithm efficiency post-segmentation.Additionally,the compression performance of the Improved Sliding Window algorithm post-segmentation showcases its suitability for trajectories of varying scales,providing reasonable compression accuracy.

关 键 词：trajectory time series unsupervised segmentation trajectory compression greedy ran-domized adaptive search 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论]