Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Highly scalable trip grouping for large-scale collective transportation systems
EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
A Hybrid Prediction Model for Moving Objects
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Mining Massive RFID, Trajectory, and Traffic Data Sets
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Towards a taxonomy of movement patterns
Information Visualization
Location prediction within the mobility data analysis environment DAEDALUS
Proceedings of the 5th Annual International Conference on Mobile and Ubiquitous Systems: Computing, Networking, and Services
Path prediction of moving objects on road networks through analyzing past trajectories
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part I
Path prediction and predictive range querying in road network databases
The VLDB Journal — The International Journal on Very Large Data Bases
Daisy: the center for data-intensive systems at Aalborg University
ACM SIGMOD Record
When and where next: individual mobility prediction
Proceedings of the First ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems
Incremental Frequent Route Based Trajectory Prediction
Proceedings of the Sixth ACM SIGSPATIAL International Workshop on Computational Transportation Science
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Emerging trends in urban mobility have accelerated the need for effective traffic prediction and management systems. The present paper proposes a novel approach to using continuously streaming moving object trajectories for traffic prediction and management. The approach continuously performs three functions for streams of moving object positions in road networks: 1) management of current evolving trajectories, 2) incremental mining of closed frequent routes, and 3) prediction of near-future locations and densities based on 1) and 2). The approach is empirically evaluated on a large real-world data set of moving object trajectories, originating from a fleet of taxis, illustrating that detailed closed frequent routes can be efficiently discovered and used for prediction.