Tutorial 2: Trajectory Data Mining

The advances in location-acquisition technologies and the prevalence of location-based 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 in business, transportation, ecology, and many more. These important applications in turn call for novel computing technologies for discovering knowledge from trajectory data. In this tutorial, we present a comprehensive, organized, and systematic survey on methodologies and algorithms on trajectory data mining. The tutorial will first give an overview of basic definitions, applications, data collection, data pre-processing, and patterns in the field of trajectory data mining. Then we will focus on three fundamental categories of trajectory patterns: (1) periodic pattern mining; (2) moving object relationship detection based on the spatial-temporal interactions which include friend relationship, follower/leader relationship, attraction/avoidance relationship, moving-together patterns, and clusters; and (3) semantic trajectory mining using external contexts. We will explore the connections, differences, and limitations of these existing techniques. Finally, we will discuss the use of trajectories in real-world applications such as recommendation, urban computing, and crime inference. We will conclude by discussing the exciting open topics in trajectory data mining.