EHSTC

Abstract

The increasing of location aware mobile devices such as vehicle navigation equipment and smart phones has enabled the collection of massive trajectories data. Movement trajectory compression has become an urgent necessity to store these data. Traditional algorithms for trajectory compression are based on the location distribution of sampling points, and often lead to intolerable error with a high compression rate. In urban road network, the movements of vehicles are usually bounded by road network. An initial thought of how to make use of semantics in trajectory compression is to represent the compressed trajectory in road segments with the entry time and the leaving time information attached. However, the movement of moving object during the road is completely abandoned. This paper has proposed an algorithm named enhanced semantic trajectory compression (EHSTC) that compress trajectories based on road semantics as well as motion feature. During chunking sampling points in a road segment, those points with great motion feature changes will be detected and stored in the feature point list of underlying road segment. The experimental result on real trajectories demonstrates the effectiveness and efficiency of the proposed solution.