In the regime of “Big Data”, data compression techniques take crucial part in preparation phase of data analysis. It is challenging because statistical properties and other characteristics need to be preserved while the size of data need to be reduced. In particular, to compress trajectory data, movement status (such as position, direction, and speed etc.) need to be retained. In this paper, we propose two different ways to reduce trajectory data size while keeping crucial information regarding the object movement intact. The first of them (KiT Algorithm) identifies “key points” in the trajectory and use them to represent the object’s movement; and the second (PaT algorithm) treats trajectory as polygons so that movement properties can be obtained by analysing the geometric properties of the polygons.