This paper introduces a hierarchical Markov model that can learn and infer a user's daily
movements through an urban community. The model uses multiple levels of abstraction in …

We present a method of learning a Bayesian model of a traveler moving through an urban
environment. This technique is novel in that it simultaneously learns a unified model of the …

In this article we discuss an assisted cognition information technology system that can learn
personal maps customized for each user and infer his daily activities and movements from …

The collection of huge amount of tracking data made possible by the widespread use of GPS
devices, enabled the analysis of such data for several applications domains, ranging from …

Both recognizing human behavior and understanding a user's mobility from sensor data are
critical issues in ubiquitous computing systems. As a kind of user behavior, the …

We describe a method called Predestination that uses a history of a driver's destinations,
along with data about driving behaviors, to predict where a driver is going as a trip …

We present PROCAB, an efficient method for Probabilistically Reasoning from Observed
Context-Aware Behavior. It models the context-dependent utilities and underlying reasons …

Understanding and discovering knowledge from GPS (Global Positioning System) traces of
human activities is an essential topic in mobility-based urban computing. We propose …

For many people, driving is a routine activity where people drive to the same destinations
using the same routes on a regular basis. Many drivers, for example, will drive to and from …

Use of taxi vehicles as mobile sensors to collect traffic information has become an important
and emerging approach to relieve congestion. Global Positioning System (GPS) trajectory …