PODS '99 Proceedings of the eighteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Indexing the positions of continuously moving objects
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
A foundation for representing and querying moving objects
ACM Transactions on Database Systems (TODS)
Dynamic Queries over Mobile Objects
EDBT '02 Proceedings of the 8th International Conference on Extending Database Technology: Advances in Database Technology
Modeling and Querying Moving Objects
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
Using GPS to learn significant locations and predict movement across multiple users
Personal and Ubiquitous Computing
Managing Moving Objects on Dynamic Transportation Networks
SSDBM '04 Proceedings of the 16th International Conference on Scientific and Statistical Database Management
A Weight-based Map Matching Method in Moving Objects Databases
SSDBM '04 Proceedings of the 16th International Conference on Scientific and Statistical Database Management
Techniques for Efficient Road-Network-Based Tracking of Moving Objects
IEEE Transactions on Knowledge and Data Engineering
Learning and inferring transportation routines
Artificial Intelligence
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
Web architecture for monitoring and visualizing mobile objects in maritime contexts
W2GIS'07 Proceedings of the 7th international conference on Web and wireless geographical information systems
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Safety and security are top concerns in maritime navigation, particularly as maritime traffic continues to grow and as crew sizes are reduced. The Automatic Identification System (AIS) plays a key role in regard to these concerns. This system, whose objective is in part to identify and locate vessels, transmits location-related information from vessels to ground stations that are part of a so-called Vessel Traffic Service (VTS), thus enabling these to track the movements of the vessels. This paper presents techniques that improve the existing AIS by offering better and guaranteed tracking accuracies at lower communication costs. The techniques employ movement predictions that are shared between vessels and the VTS. Empirical studies with a prototype implementation and real vessel data demonstrate that the techniques are capable of significantly improving the AIS.