Efficient Algorithms for Shortest Paths in Sparse Networks
Journal of the ACM (JACM)
Advances in Minimum Description Length: Theory and Applications (Neural Information Processing)
Advances in Minimum Description Length: Theory and Applications (Neural Information Processing)
On map-matching vehicle tracking data
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Trajectory Similarity Search in Spatial Networks
IDEAS '06 Proceedings of the 10th International Database Engineering and Applications Symposium
Nonmaterialized motion information in transport networks
ICDT'05 Proceedings of the 10th international conference on Database Theory
Efficient k-nearest neighbor search on moving object trajectories
The VLDB Journal — The International Journal on Very Large Data Bases
SeTraStream: semantic-aware trajectory construction over streaming movement data
SSTD'11 Proceedings of the 12th international conference on Advances in spatial and temporal databases
Encoding network-constrained travel trajectories using routing algorithms
International Journal of Knowledge and Web Intelligence
Map-matched trajectory compression
Journal of Systems and Software
Semantic trajectories modeling and analysis
ACM Computing Surveys (CSUR)
EHSTC: an enhanced method for semantic trajectory compression
Proceedings of the 4th ACM SIGSPATIAL International Workshop on GeoStreaming
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The wide usage of location aware devices, such as GPS-enabled cellphones or PDAs, generates vast volumes of spatiotemporal streams modeling objects movements, raising management challenges, such as efficient storage and querying. Therefore, compression techniques are inevitable also in the field of moving object databases. Moreover, due to erroneous measurements from GPS devices, the problem of matching the location recordings with the underlying traffic network has recently gained the attention of the research community. So far, the proposed compression techniques are not designed for network constrained moving objects, while map matching algorithms do not consider compression issues. In this paper, we propose solutions tackling the combined, map matched trajectory compression problem, the efficiency of which is demonstrated through an experimental evaluation using a real trajectory dataset.