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 patts on road network.By treating the mined patts 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 patts for much low space cost.Mean-while,the patts are also utilized to improve the performance of two trajectory applications,range query and clustering,without decom-pression overhead.Extensive experiments on real trajectory datasets validate that CLEAN significantly outperforms existing state-of-art approaches in terms of spatial-temporal com-pression and trajectory applications.