Introduction to algorithms
Trajectory clustering: a partition-and-group framework
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
On-line discovery of hot motion paths
EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
Mining frequent trajectory patterns in spatial-temporal databases
Information Sciences: an International Journal
Traffic density-based discovery of hot routes in road networks
SSTD'07 Proceedings of the 10th international conference on Advances in spatial and temporal databases
A graph-based approach to vehicle trajectory analysis
Journal of Location Based Services - GeoVA(t) - Geospatial visual analytics: focus on time. Special issue of the ICA Commission on GeoVisualisation
Discovering popular routes from trajectories
ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering
Finding long and similar parts of trajectories
Computational Geometry: Theory and Applications
Computing with Spatial Trajectories
Computing with Spatial Trajectories
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
Fast and exact network trajectory similarity computation: a case-study on bicycle corridor planning
Proceedings of the 2nd ACM SIGKDD International Workshop on Urban Computing
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Given a set of GPS trajectories on a road network, the goal of the k-Primary Corridors (k-PC) problem is to summarize trajectories into k groups, each represented by its most central trajectory. This problem is important to a variety of domains, such as transportation services interested in finding primary corridors for public transportation or greener travel (e.g., bicycling) by leveraging emerging GPS trajectory datasets. Related trajectory mining approaches, e.g., density or frequency based hot-routes, focus on anomaly detection rather than summarization and may not be effective for the k-PC problem. The k-PC problem is challenging due to the computational cost of creating the track similarity matrix. A naïve graph-based approach to compute a single element of this track similarity matrix requires multiple invocations of common shortest-path algorithms (e.g., Dijkstra). To reduce the computational cost of creating this track similarity matrix, we propose a novel algorithm that switches from a graph-based view to a matrix-based view, computing each element in the matrix with a single invocation of a shortest-path algorithm. Experimental results show that these ideas substantially reduce computational cost without altering the results.