A hybrid compression framework for large scale trajectory data in road networks

Abstract

A Hybrid compression framework of trajectory data (HCFT) is proposed for effective compression of trajectory data with road network limited. It's different from the present researches which mainly focus on compression of single trajectory, and further takes data redundancy raised by the similarity of movement pattern of moving objects into consideration. HCFT divides the redundancy of trajectory data into Single trajectory redundancy (STR) and Multiple trajectories redundancy (MTR) and compresses them in a hybrid way (i.e. synchronous compression for STR at first and then asynchronous compression for MTR). We propose an asynchronous extraction algorithm for MTR based on frequent Road track subsequence (RTS), which replaces similar movement route by RTS, with the complexity of calculation significantly reduced. HCFT can not only gain higher compression ratio, but also ensure effectiveness of compressed trajectory. We also verify effectiveness and superiority of the new method according to the experiments of real trajectory dataset.