The R*-tree: an efficient and robust access method for points and rectangles
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
Spatio-temporal composition and indexing for large multimedia applications
Multimedia Systems
Indexing the positions of continuously moving objects
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Novel Approaches in Query Processing for Moving Object Trajectories
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Indexing the Current Positions of Moving Objects Using the Lazy Update R-tree
MDM '02 Proceedings of the Third International Conference on Mobile Data Management
Indexing of network constrained moving objects
GIS '03 Proceedings of the 11th ACM international symposium on Advances in geographic information systems
STRIPES: an efficient index for predicted trajectories
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Indexing the Trajectories of Moving Objects in Networks*
Geoinformatica
Techniques for Efficient Road-Network-Based Tracking of Moving Objects
IEEE Transactions on Knowledge and Data Engineering
Trajectory Indexing Using Movement Constraints
Geoinformatica
ANTS: Efficient Vehicle Locating Based on Ant Search in ShanghaiGrid
ICPP '07 Proceedings of the 2007 International Conference on Parallel Processing
The TPR*-tree: an optimized spatio-temporal access method for predictive queries
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
MR-Tree: a cache-conscious main memory spatial index structure for mobile GIS
W2GIS'04 Proceedings of the 4th international conference on Web and Wireless Geographical Information Systems
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The past composite structures for managing spatio-temporal data based on the road network usually use road segment as units to divide moving objects into different groups. This scheme causes inaccurate description about vehicles' current segments, and also takes high update cost caused by R-Tree's inherent property. In this paper, we propose an improved partition method, which uses a new division unit called Cross Region (CR) to group the moving objects inside road networks, and then a new structure called CR-Tree is also proposed to be the static part of the composite structure. With CR, the position description for vehicles becomes more accurate, and the update cost could also be decreased.