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
Indexing the positions of continuously moving objects
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
A road network embedding technique for k-nearest neighbor search in moving object databases
Proceedings of the 10th ACM international symposium on Advances in geographic information systems
MV3R-Tree: A Spatio-Temporal Access Method for Timestamp and Interval Queries
Proceedings of the 27th 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 Moving Objects for Location-Based Services
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
STRIPES: an efficient index for predicted trajectories
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Continuous nearest neighbor monitoring in road networks
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Distance indexing on road networks
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
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
Query and update efficient B+-tree based indexing of moving objects
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
The Bdual-Tree: indexing moving objects by space filling curves in the dual space
The VLDB Journal — The International Journal on Very Large Data Bases
ST2B-tree: a self-tunable spatio-temporal b+-tree index for moving objects
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Monitoring Aggregate k-NN Objects in Road Networks
SSDBM '08 Proceedings of the 20th international conference on Scientific and Statistical Database Management
Index Method for Tracking Network-Constrained Moving Objects
KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part II
Continuous Reverse Nearest Neighbor Queries on Moving Objects in Road Networks
WAIM '08 Proceedings of the 2008 The Ninth International Conference on Web-Age Information Management
Snapshot location-based query processing on moving objects in road networks
Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems
Indexing of Moving Objects on Road Network Using Composite Structure
KES '07 Knowledge-Based Intelligent Information and Engineering Systems and the XVII Italian Workshop on Neural Networks on Proceedings of the 11th International Conference
Indexing of Continuously Moving Objects on Road Networks
IEICE - Transactions on Information and Systems
The RUM-tree: supporting frequent updates in R-trees using memos
The VLDB Journal — The International Journal on Very Large Data Bases
Indexing Moving Objects Using Short-Lived Throwaway Indexes
SSTD '09 Proceedings of the 11th International Symposium on Advances in Spatial and Temporal Databases
Update-efficient indexing of moving objects in road networks
Geoinformatica
Trees or grids?: indexing moving objects in main memory
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Effective density queries for moving objects in road networks
APWeb/WAIM'07 Proceedings of the joint 9th Asia-Pacific web and 8th international conference on web-age information management conference on Advances in data and web management
Path prediction and predictive range querying in road network databases
The VLDB Journal — The International Journal on Very Large Data Bases
Fast indexing and updating method for moving objects on road networks
WISEW'03 Proceedings of the Fourth international conference on Web information systems engineering workshops
Time constrained range search queries over moving objects in road networks
Proceedings of the 8th International Conference on Advances in Mobile Computing and Multimedia
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
Using compressed index structures for processing moving objects in large spatio-temporal databases
Journal of Systems and Software
Indexing moving objects on road networks in p2p and broadcasting environments
W2GIS'06 Proceedings of the 6th international conference on Web and Wireless Geographical Information Systems
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The advances in communication and positioning device technologies have made it possible to track the locations of moving objects, such as vehicles equipped with GPS. As a result, a new series of applications and services have been commenced into people's life. One popular application is the real-time traffic system which provides current road condition and traffic jam information to commuters. To further enhance this location-based experience, this paper proposes an advanced type of service which can predict traffic jams so that commuters can plan their trips more effectively. In particular, traffic prediction is realized by a new type of query, termed as the predictive line query, which estimates the amount of vehicles entering a querying road segment at a specified future timestamp and helps query issuers adjust their travel plans in a timely manner. Only a handful of existing work can efficiently and effectively handle such queries since most methods are designed for objects moving freely in the Euclidean space instead of under road-network constraints. Taking the road network topology and object moving patterns into account, we propose a hybrid index structure, the RD-tree, which employs an R*-tree for network indexing and direction-based hash tables for managing vehicles. We also develop a ring-query-based algorithm to answer the predictive line query. We have conducted an extensive experimental study which demonstrates that our approach significantly outperforms existing work in terms of both accuracy and time efficiency.