E3TP: A Novel Trajectory Prediction Algorithm in Moving Objects Databases

Conference Paper · April 2009with17 Reads
DOI: 10.1007/978-3-642-01393-5_9 · Source: DBLP
Conference: Intelligence and Security Informatics, Pacific Asia Workshop, PAISI 2009, Bangkok, Thailand, April 27, 2009. Proceedings

    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.