Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Computing longest duration flocks in trajectory data
GIS '06 Proceedings of the 14th annual ACM international symposium on Advances in geographic information systems
Computational Geometry: Theory and Applications
Continuous Clustering of Moving Objects
IEEE Transactions on Knowledge and Data Engineering
Discovery of convoys in trajectory databases
Proceedings of the VLDB Endowment
Detecting Commuting Patterns by Clustering Subtrajectories
ISAAC '08 Proceedings of the 19th International Symposium on Algorithms and Computation
DASFAA'06 Proceedings of the 11th international conference on Database Systems for Advanced Applications
On discovering moving clusters in spatio-temporal data
SSTD'05 Proceedings of the 9th international conference on Advances in Spatial and Temporal Databases
Correlation analysis of discrete motions
GIScience'06 Proceedings of the 4th international conference on Geographic Information Science
Exact algorithms for partial curve matching via the Fréchet distance
SODA '09 Proceedings of the twentieth Annual ACM-SIAM Symposium on Discrete Algorithms
Approximating the Fréchet distance for realistic curves in near linear time
Proceedings of the twenty-sixth annual symposium on Computational geometry
The frechet distance revisited and extended
Proceedings of the twenty-seventh annual symposium on Computational geometry
Jaywalking your dog: computing the Fréchet distance with shortcuts
Proceedings of the twenty-third annual ACM-SIAM symposium on Discrete Algorithms
How to walk your dog in the mountains with no magic leash
Proceedings of the twenty-eighth annual symposium on Computational geometry
Algorithms for hotspot computation on trajectory data
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
The fréchet distance revisited and extended
ACM Transactions on Algorithms (TALG)
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We study the problem of detecting a single file behavior in a set of trajectories. A group of entities is moving in single file if they are following each other, one behind the other. This movement pattern occurs often, among animals, humans, and vehicles. It is challenging to detect because it does not have a fixed layout. In this paper we first model the notion of following behind, on which we base our definition of single file. We present efficient algorithms for detecting following behind and single file behaviors. We test and evaluate these algorithms on real and generated test data.