Trajectory clustering with mixtures of regression models
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
ACM Computing Surveys (CSUR)
Proceedings of the tenth 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
Continuous Clustering of Moving Objects
IEEE Transactions on Knowledge and Data Engineering
T-drive: driving directions based on taxi trajectories
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
Pathlet learning for compressing and planning trajectories
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
From taxi GPS traces to social and community dynamics: A survey
ACM Computing Surveys (CSUR)
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We propose a flexible mini-shuttle like transportation system called flexi, with routes formed by analyzing passenger trip data from a large set of taxi trajectories. The usage of public transportation is declining as often it no longer matches with individual needs. Thus, the flexi system provides a transportation mode in between buses and taxis so that inconvenience in switching to the system can be minimized overall. To generate flexi routes, we propose a two-phase approach. In the first phase, a fast diameter-constrained agglomerative clustering algorithm is developed and applied to the set of trips derived from the GPS data. This phase identifies a set of heavily traveled spatio-temporal trip clusters called hot lines. In the second phase, a directed acyclic graph is constructed from the hot lines. Then, an optimal single flexi route discovery algorithm on graph searching is proposed. Multiple routes are discovered by iteratively applying the single routing algorithm. Extensive experiments using a large set of real taxi trajectory data show that the flexi system can save a large percentage of trip mileage.