Discovering Frequent Mobility Patterns on Moving Object Data
In this paper, the authors consider the problem of efficiently discovering and detecting frequent mobility patterns on moving object data. Their proposed approach is key for mobility applications, such as applications that need to discover and explain movement patterns of a set of moving objects (e.g. traffic management, birds migration and disease spreading). In this sense, they developed a method that performs density based clustering on trajectory data at regular time intervals, then they analyze clusters evolution, which is characterized by appear, disappear, expand, shrink, split, merge and survive.