Semantic Understanding of Spatial Trajectories

The advances in location-acquisition technologies and the prevalence of locationbased services have generated massive spatial trajectory data, which represent the mobility of a diversity of moving objects, such as people, vehicles, and animals. Such trajectories offer us unprecedented information to understand moving objects and locations that could benefit a broad range of applications. These important applications in turn calls for novel computing technologies for discovering knowledge from trajectory data. Under the circumstances, trajectory data mining has become an increasingly important research theme in the past decade [10]. Extensive research has been done in the field of trajectory data mining with many interesting patterns have been proposed and studied. However, most existing studies focus on only trajectory data and did not consider rich spatial-temporal contexts that are associated with trajectories. As a consequence, trajectory patterns detected from existing methods could be trivial. Let’s examine an example below.