Managing Moving Objects on Transportation Networks

large numbers of moving objects and continuous queries characterize moving object environments. Efficient evaluation of these queries in response to the movement of the objects is important for supporting acceptable response times. In such environments, the traditional approaches suffer from the need for frequent updates and thereby results in poor performance. In this paper we present a novel approach for approximate query processing about the present, past, and the future in moving objects databases for objects that constrained to move on transportation networks. Our structure for objects moving on network (MON) is a simple structure with minimum update and processing requirements; it uses the edges movement constraints of the networks to reduce the update frequency and adapts prediction skim - that is built on the cellular automation model - to predict future trajectories of objects in more accuracy than linear prediction. This prediction model will be built only if needed for specific edges of the network decided by queries about the future trajectories of objects. In particular, we first expand our previous work (TNR+-Tree in [1] to index line edges of transportation networks as well as historical positions of objects moving on those edges for queries about past and present trajectories. Second for answering queries about future trajectories; edges of the network related to the query is simulated using Cellular Automation(CA) as in [2], which make use of the constraints of the network and the stochastic behavior of the traffic to predict future positions with minimum location update and reasonable certainty. Finally, we experimentally prove the effectiveness and applicability of our technique using a realistic simulation.