Rapid advances of sensing and positioning technologies have provided us with an increasing amount of trajectory data collected from human movements, animal traces, and traffic. Understanding such large-scale trajectory data together with their surrounding contexts (e.g., location information, local events, weather and environment) could benefit a number of important applications. For example, a semantic understanding of human trajectories can help profiling a person's interest, socioeconomic status and health conditions; mining traffic patterns w.r.t. local events and weather conditions can lead to a more resilient transportation system; and studying how animal movements respond to environmental changes can advance our understanding of the ecological system. This project investigates data mining algorithms and provides solutions toward semantic trajectory mining with rich spatial-temporal contexts. The results will have broader impacts in other disciplines such as social science, health, transportation, and ecology through PI's interdisciplinary collaborations.

This project seeks innovative solutions for trajectory data mining. State-of-the-art trajectory data mining methods have been largely limited to studying only trajectory data without considering the rich spatial-temporal contextual information. Such a limitation often results in detecting trivial or even erroneous patterns. For example, anomalies (e.g., heavy traffic or detouring trajectories) may actually be expected if considering the contexts such as big football games or extreme weather. To incorporate contexts in trajectory mining, the key challenge is to model the implicit and complications correlations between contexts and trajectories given the sparse and noisy observations on both trajectories and contexts. To correctly and fully utilize the rich contexts in analyzing trajectory data, this project has two research aims: (1) trajectory annotation using contexts and (2) trajectory mining with contexts. In the first aim, the objective is to associate trajectories with relevant static or dynamic contexts. In the second aim, the researchers will re-visit trajectory mining algorithms and innovate three important mining techniques: recurrence pattern mining, anomaly detection, and trajectory clustering. New data mining techniques will be developed to incorporate the annotated contexts in the data mining process. The project will advance the state of the art in trajectory data mining and enrich general data mining principles.