A Semantics-Based Trajectory Segmentation Simplification Method

Abstract

With the development of mobile positioning technology, a large amount of mobile trajectory data has been generated. Therefore, to store, process and mine trajectory data in a better way, trajectory data simplification is imperative. Current trajectory data simplification methods are either based on spatiotemporal features or semantic features, such as road network structure, but they do not consider semantic features of a trajectory stop. To overcome this limitation, this study presents a trajectory segmentation simplification method based on stop features. The proposed method first extracts the stop feature of a trajectory, then divides the trajectory into move segments and stop segments based on the stop features, and finally simplifies the obtained segments. The proposed method is verified by experiments on personal trajectory data and taxi trajectory data. Compared with the classic spatiotemporal simplification method, the proposed method has higher spatiotemporal and semantic accuracy under different simplification scales. The proposed method is especially suitable for trajectory data with more stop features.