A Dynamic Mobility Histogram Construction Method Based on Markov Chains

With the recent progress of spatial information technologies and communication technologies, it has become easier to track positions of a large number of moving objects in real-time. Mobility statistics plays an important role in the interactive analysis of a large collection of moving objects trajectories and its use of movement pattern prediction. The development of an effective mobility statistics measure and its efficient computation method are critical issues. Thus, we propose an approach for constructing a mobility histogram to summarize a number of moving object trajectories. The histogram is based on a mobility statistics model called the Markov chain model. To facilitate an interactive analysis performed by a user, we provide a mobility histogram data cube-like logical representation and support an OLAP-style analysis. Since trajectory data is often received continuously as a trajectory stream, we have to support dynamic histogram construction and maintenance. We introduce a tree structure as the physical representation of a histogram and present histogram construction and maintenance methods that work efficiently within the given upper-bound size. We evaluate the performance and the precision of the proposed method by means of experiments