Understanding moving object behaviors, also known as trajectory semantics, is an important problem that affects many decision making applications. Previous works typically identify such behaviors by using known landmarks, also termed as Regions of Interest (ROIs) (parks, museums, malls, etc.) that are geographically collocated with the trajectory. The main objective of this thesis is to identify trajectory semantics, by looking only at the trajectory data, i.e., without assuming pre-knowledge of ROIs.

We first present a new trajectory behavior, by defining the notion of dwell regions. A region R is a dwell region for a moving object O if, given a threshold distance d and duration t, every point of R remains within distance d of O for at least time t. Clearly, points within R are likely to be of interest to O. We present methods for determining dwell regions for both streaming and archived data. Next, we introduce a novel query that can be used to track conclaves (i.e., secret meetings) of a group of moving object. In this environment we assume only partial observations of the individual object movements, within the context of a local transportation network. This is a realistic assumption due to sparsely-distributed surveillance cameras or lack of observations in general. Given such limited observations we seek to infer the set of all possible conclaves. The third chapter of the thesis addresses ROI identification. An ROI is typically defined as a region where a large number of moving objects remain for at least a given time interval. Previous methods require sequential scanning of the entire dataset to find ROIs when the semantics (number of objects, time duration) change. Here, we propose a novel method based on object density, to efficiently identify ROIs with arbitrary semantics; this method scans the dataset only once.

We also revisit indexing of trajectories using Hilbert curves. Instead of using minimum bounding rectangles we present methods to use Hilbert curves to index trajectory polylines. Our method outperforms the state of the art methods for spatial range queries by two to fifteen times. Even though such transformation does not preserve the Euclidean distance, we show, that our approach can also be used to efficiently answer kNN queries.