A Compression method for Trajectory data based on Prediction Model (CTPM) was proposed to improve compression efficiency of massive trajectory data in road network environment. The temporal information and spatial information of the trajectory data were respectively compressed so that the compressed trajectory data was lossless in the spatial dimension and the error was bounded in the time dimension. In terms of space, the Prediction by Partial Matching (PPM) algorithm was used to predict the possible position of the next moment by the part trajectory that had been driven. And then the predicted road segments were deleted to reduce the storage cost. In terms of time, the statistical traffic speed model of different time intervals was constructed according to the periodic feature of the traffic condition to predict the required time for moving objects to enter the next section. And then the compression process was performed by deleting the road section information which predicted time error was smaller than the given threshold. In the comparison experiments with Paralleled Road-network-based trajectory comprESSion (PRESS) algorithm, the average compression ratio of CTPM was increased by 43% in space and 1.5% in time, and the temporal error was decreased by 9.5%. The experimental results show that the proposed algorithm can effectively reduce the compression time and compression error while improving the compression ratio.