An Efficient Indexing Technique for Location Prediction of Moving Objects

Abstract

The necessity of the future index is increasing to predict the future location of moving objects promptly for various location-based services. However, the prediction performance of most future indexes is lowered by the heavy load of extensive future trajectory search in long-range future queries, and their index maintenance cost is high due to the frequent update of future trajectories. Thus, this paper proposes the Probability Cell Trajectory-Tree (PCT-Tree), a cell-based future indexing technique for efficient long-range future location prediction. The PCT-Tree reduces the size of index by building the probability of extensive past trajectories in the unit of cell, and predicts reliable future trajectories using information on past trajectories. Therefore, the PCT-Tree can minimize the cost of communication in future trajectory prediction and the cost of index rebuilding for updating future trajectories. Through experiment, we proved the superiority of the PCT-Tree over existing indexing techniques in the performance of long-range future queries.