Convex Optimization
Hidden Markov map matching through noise and sparseness
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
T-drive: driving directions based on taxi trajectories
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
Sensing urban mobility with taxi flow
Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-Based Social Networks
Median trajectories using well-visited regions and shortest paths
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
A greener transportation mode: flexible routes discovery from GPS trajectory data
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Computing with Spatial Trajectories
Computing with Spatial Trajectories
Of motifs and goals: mining trajectory data
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
Large-scale joint map matching of GPS traces
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Compact representation of GPS trajectories over vectorial road networks
SSTD'13 Proceedings of the 13th international conference on Advances in Spatial and Temporal Databases
Model-driven matching and segmentation of trajectories
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
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The wide deployment of GPS devices has generated gigantic datasets of pedestrian and vehicular trajectories. These datasets offer great opportunities for enhancing our understanding of human mobility patterns, thus benefiting many applications ranging from location-based services (LBS) to transportation system planning. In this work, we introduce the notion of pathlet for the purpose of compressing and planning trajectories. Given a collection of trajectories on a roadmap as input, we seek to compute a compact dictionary of pathlets so that the number of pathlets that are used to represent each trajectory is minimized. We propose an effective approach whose complexity is linear in the number of trajectories. Experimental results show that our approach is able to extract a compact pathlet dictionary such that all trajectories can be represented by the concatenations of a few pathlets from the dictionary. We demonstrate the usefulness of the learned pathlet dictionary in route planning.