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Brussels, Belgium Belgium
Dec. 10, 2012 to Dec. 10, 2012
ISBN: 978-1-4673-5164-5
pp: 910-913
ABSTRACT
Mining high resolution trajectories from moving vehicles can provide insightful analytics and enable location based decision making. In this paper, we introduce a trajectory mining prototype system to generate the trajectory heat map at aggregated level for the online spatial-temporal analytics. The proposed method is scalable and efficient since we develop a mechanism using a small subset of trajectory points to capture all trajectory patterns. We experimentally verified the applicability and scalability of this system with large scale real world dataset.
INDEX TERMS
Trajectory, Heating, Vehicles, Roads, Global Positioning System, Data mining, Scalability, Spatio-temporal analytics, Trajectory mining, Transit point
CITATION
Songhua Xing, Xuan Liu, Qing He, Arun Hampapur, "Mining Trajectories for Spatio-temporal Analytics", ICDMW, 2012, 2013 IEEE 13th International Conference on Data Mining Workshops, 2013 IEEE 13th International Conference on Data Mining Workshops 2012, pp. 910-913, doi:10.1109/ICDMW.2012.25
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