SPADE: an efficient algorithm for mining frequent sequences
Machine Learning
A Framework for Generating Network-Based Moving Objects
Geoinformatica
PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth
Proceedings of the 17th International Conference on Data Engineering
Indexing of Moving Objects for Location-Based Services
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Prediction and indexing of moving objects with unknown motion patterns
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Managing uncertainty in moving objects databases
ACM Transactions on Database Systems (TODS)
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Frequent Trajectories of Moving Objects for Location Prediction
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Mining interesting locations and travel sequences from GPS trajectories
Proceedings of the 18th international conference on World wide web
E3TP: A Novel Trajectory Prediction Algorithm in Moving Objects Databases
PAISI '09 Proceedings of the Pacific Asia Workshop on Intelligence and Security Informatics
WhereNext: a location predictor on trajectory pattern mining
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Prediction of moving object location based on frequent trajectories
ISCIS'06 Proceedings of the 21st international conference on Computer and Information Sciences
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Trajectory prediction (TP) of moving objects has grown rapidly to be a new exciting paradigm. However, existing prediction algorithms mainly employ kinematical models to approximate real world routes and always ignore spatial and temporal distance. In order to overcome the drawbacks of existing TP approaches, this study proposes a new trajectory prediction algorithm, called HDTP (Hotspot Distinct Trajectory Prediction). It works as: (1) mining the hotspot districts from trajectory data sets; (2) extracting the trajectory patterns from trajectory data; and (3) predicting the location of moving objects by using the common movement patterns. By comparing this proposed approach to E3TP, the experiments show HDTP is an efficient and effective algorithm for trajectory prediction, and its prediction accuracy is about 14.7% higher than E3TP.