Modeling historical and future movements of spatio-temporal objects in moving objects databases
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Node and edge selectivity estimation for range queries in spatial networks
Information Systems
Update-efficient indexing of moving objects in road networks
Geoinformatica
Modeling and prediction of moving region trajectories
Proceedings of the ACM SIGSPATIAL International Workshop on GeoStreaming
Key issues and theoretical framework on moving objects data mining
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
Model-based object tracking in wireless sensor networks
Wireless Networks
Fast RSVP: Efficient RSVP Mobility Support for Mobile IPv6
Wireless Personal Communications: An International Journal
Computers & Mathematics with Applications
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Advances in wireless sensor networks and positioning technologies enable traffic management (e.g. routing traffic) that uses real-time data monitored by GPS-enabled cars. Location management has become an enabling technology in such application. The location modeling and trajectory prediction of moving objects are the fundamental components of location management in mobile locationaware applications. In this paper, we model the road network and moving objects in a graph of cellular automata (GCA), which makes full use of the constraints of the network and the stochastic behavior of the traffic. A simulation-based method based on graphs of cellular automata is proposed to predict future trajectories. Our technique strongly differs from the linear prediction method, which has low prediction accuracy and requires frequent updates when applied to real traffic with velocity changes. The experiments, carried on two different datasets, show that the simulation-based prediction method provides higher accuracy than the linear prediction method.