Conference Paper

E3TP: A Novel Trajectory Prediction Algorithm in Moving Objects Databases.

DOI: 10.1007/978-3-642-01393-5_9 Conference: Intelligence and Security Informatics, Pacific Asia Workshop, PAISI 2009, Bangkok, Thailand, April 27, 2009. Proceedings
Source: DBLP

ABSTRACT Prediction of uncertain trajectories in moving objects databases has recently become a new paradigm for tracking wireless
and mobile devices in an accurate and efficient manner, and is critical in law enforcement applications such as criminal tracking
analysis. However, existing approaches for prediction in spatio-temporal databases focus on either mining frequent sequential
patterns at a certain geographical position, or constructing kinematical models to approximate real-world routes. The former
overlooks the fact that movement patterns of objects are most likely to be local, and constrained in some certain region,
while the later fails to take into consideration some important factors, e.g., population distribution, and the structure
of traffic networks. To cope with those problems, we propose a general trajectory prediction algorithm called E3TP (an Effective, Efficient, and Easy Trajectory Prediction algorithm), which contains four main phases: (i) mining “hotspot” regions from moving objects databases; (ii) discovering frequent sequential routes in hotspot areas; (iii) computing the speed of a variety of moving objects; and (iv) predicting the dynamic motion behaviors of objects. Experimental results demonstrate that E3TP is an efficient and effective algorithm for trajectory prediction, and the prediction accuracy is about 30% higher than
the naive approach. In addition, it is easy-to-use in real-world scenarios.

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    ABSTRACT: Existing trajectory 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 trajectory prediction approaches, this paper proposes a novel trajectory prediction algorithm. It works as: (1) mining the interesting regions 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 our approach is an efficient and effective algorithm for trajectory prediction.
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    ABSTRACT: 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.
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