Optimizing predictive queries on moving objects under road-network constraints
DEXA'11 Proceedings of the 22nd international conference on Database and expert systems applications - Volume Part I
Continuous reverse k nearest neighbors queries in Euclidean space and in spatial networks
The VLDB Journal — The International Journal on Very Large Data Bases
Efficiently processing snapshot and continuous reverse k nearest neighbors queries
The VLDB Journal — The International Journal on Very Large Data Bases
Predictive line queries for traffic prediction
Transactions on Large-Scale Data- and Knowledge-Centered Systems VI
Continuous maximal reverse nearest neighbor query on spatial networks
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
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Continuous Reverse nearest neighbor (CRNN) query processing in road networks has received considerable attention of location-based services on moving objects. Most RNN queries in traffic networks only consider the moving objects as the static objects at each timestamp, which is difficult to adapt to different traffic situations such as traffic jams. Relying on the heuristic expansion strategy, we propose a new CRNN query algorithm that can be adapted to the dynamic traffic situation updating. PMR quad-tree and Dijkstra search strategy are adopted in RNN query algorithm. The experiments with real datasets show that the algorithm has a high efficiency and scalability.