Update-efficient indexing of moving objects in road networks

Recent advances in wireless sensor networks and positioning technologies have boosted new applications that manage moving objects. In such applications, a dynamic index is often built to expedite evaluation of spatial queries. However, the development of efficient indexes is a challenge due to frequent object movement. In this paper, we propose a new update-efficient index method for moving objects in road networks. We introduce a dynamic data structure, called adaptive unit, to group neighboring objects with similar movement patterns. To reduce updates, an adaptive unit captures the movement bounds of the objects based on a prediction method, which considers road-network constraints and the stochastic traffic behavior. A spatial index (e.g., R-tree) for the road network is then built over the adaptive unit structures. Simulation experiments, carried on two different datasets, show that an adaptive-unit based index is efficient for both updating and querying performances.