A Framework for Generating Network-Based Moving Objects
Geoinformatica
On map-matching vehicle tracking data
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Time-focused clustering of trajectories of moving objects
Journal of Intelligent Information Systems
Trajectory clustering: a partition-and-group framework
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
ESA'06 Proceedings of the 14th conference on Annual European Symposium - Volume 14
A tutorial on spectral clustering
Statistics and Computing
Mobility, Data Mining and Privacy: Geographic Knowledge Discovery
Mobility, Data Mining and Privacy: Geographic Knowledge Discovery
Convoy Queries in Spatio-Temporal Databases
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Multi-level Algorithms for Modularity Clustering
SEA '09 Proceedings of the 8th International Symposium on Experimental Algorithms
Map-matching for low-sampling-rate GPS trajectories
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
NNCluster: an efficient clustering algorithm for road network trajectories
DASFAA'10 Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part II
Computer Science Review
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Clustering trajectory data attracted considerable attention in the last few years. Most of prior work assumed that moving objects can move freely in an euclidean space and did not consider the eventual presence of an underlying road network and its influence on evaluating the similarity between trajectories. In this paper, we present an approach to clustering such network-constrained trajectory data. More precisely we aim at discovering groups of road segments that are often travelled by the same trajectories. To achieve this end, we model the interactions between segments w.r.t. their similarity as a weighted graph to which we apply a community detection algorithm to discover meaningful clusters. We showcase our proposition through experimental results obtained on synthetic datasets.