Measuring similarity between trajectories of mobile objects

With technological progress we encounter more available data on the locations of moving objects and therefore the need for mining moving objects data is constantly growing. Mining spatio-temporal data can direct products and services to the right customers at the right time; it can also be used for resources optimization or for understanding mobile patterns. In this chapter, we cluster trajectories in order to find movement patterns of mobile objects. We use a compact representation of a mobile trajectory, which is based on a list of minimal bounding boxes (MBBs). We introduce a new similarity measure between mobile trajectories and compare it empirically to an existing similarity measure by clustering spatio-temporal data and evaluating the quality of resulting clusters and the algorithm run times.