CLEAN:Frequent Pattern-Based Trajectory Compression and Computation on Road Networks  

作　　者：Peng Zhao Qinpei Zhao Chenxi Zhang Gong Su Qi Zhang Weixiong Rao 

机构地区：[1]Tongji University,Shanghai 201804,China [2]IBM T.J.Watson Research Center,New York 10598,USA

出　　处：《China Communications》2020年第5期119-136,共18页中国通信（英文版）

基　　金：National Natural Science Foundation of China (Grant No. 61772371,No. 61972286)

摘　　要：The volume of trajectory data has become tremendously huge in recent years. How to effectively and efficiently maintain and compute such trajectory data has become a challenging task. In this paper, we propose a trajectory spatial and temporal compression framework, namely CLEAN. The key of spatial compression is to mine meaningful trajectory frequent patterns on road network. By treating the mined patterns as dictionary items, the long trajectories have the chance to be encoded by shorter paths, thus leading to smaller space cost. And an error-bounded temporal compression is carefully designed on top of the identified spatial patterns for much low space cost. Meanwhile, the patterns are also utilized to improve the performance of two trajectory applications, range query and clustering, without decompression overhead. Extensive experiments on real trajectory datasets validate that CLEAN significantly outperforms existing state-of-art approaches in terms of spatial-temporal compression and trajectory applications.

关 键 词：trajectory compression pattern mining spatial-temporal compressions range query CLUSTERING 

分 类 号：U495[交通运输工程—交通运输规划与管理]