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