Trajectory classification, i.e., model construction for predicting the class labels of moving objects based on their trajectories and other features, has many important, real-world applications. A number of methods have been reported in the literature, but due to using the shapes of whole trajectories for classification, they have limited classification capability when discriminative features appear at parts of trajectories or are not relevant to the shapes of trajectories. These situations are often observed in long trajectories spreading over large geographic areas.
Since an essential task for effective classification is generating discriminative features, a feature generation framework TraClass for trajectory data is proposed in this paper, which generates a hierarchy of features by partitioning trajectories and exploring two types of clustering: (1) region-based and (2) trajectory-based. The former captures the higher-level region-based features without using movement patterns, whereas the latter captures the lower-level trajectory-based features using movement patterns. The proposed framework overcomes the limitations of the previous studies because trajectory partitioning makes discriminative parts of trajectories identifiable, and the two types of clustering collaborate to find features of both regions and sub-trajectories. Experimental results demonstrate that TraClass generates high-quality features and achieves high classification accuracy from real trajectory data.
Int'l Conf. on Very Large Data Bases, v.1, no.1, pp.1081 - 1094