Geometry of Interest (GOI): Spatio-Temporal Destination Extraction and Partitioning in GPS Trajectory Data

Noname manuscript No. (will be inserted by the editor) Abstract Nowadays large amounts of GPS trajectory data is being continuously collected by GPSenabled devices such as vehicles navigation systems and mobile phones. GPS trajectory data is useful for applications such as traffic management, location forecasting, and itinerary planning. Such applications often need to extract the time-stamped Sequence of Visited Locations (SVLs) of the mobile objects. The nearest neighbor query (NNQ) is the most applied method for labeling the visited locations based on the IDs of the POIs in the process of SVL generation. NNQ in some scenarios is not accurate enough. To improve the quality of the extracted SVLs, instead of using NNQ, we label the visited locations as the IDs of the POIs which geometrically intersect with the GPS observations. In this paper we propose a novel method for estimating the POIs and their GOIs, which consists of three phases: (i) extracting the geometries of the stay regions; (ii) constructing the geometry of destination regions based on the extracted stay regions; and (iii) constructing the GOIs based on the geometries of the destination regions. Using the geometric similarity to known GOIs as the major evaluation criterion, the experiments we performed using long-term GPS trajectory data show that our method outperforms the existing approaches. Keywords Trajectory Data, Spatio-Temporal Partitioning, Geometry of Interest, Time-Value, Time-Weighted Centroid, Destination Extraction 1 Introduction In recent years, GPS trajectory data has become abundant due to the many GPS enabled devices used on a daily basis. Mining these GPS trajectories for gathering useful information for applications has received a growing amount of attention in the recent literature. In this field, researchers have tried to derive knowledge for solving practical problems (e.g. The applications dealing with data analysis on trajectory data often need to have access to information about the significant places which a mobile object frequently travels and stay. These significant places are referred to as the points of interest (POIs).