This work has two objectives: a) to predict people's future locations, and b) to predict when they will be at given locations. Current location-based applications react to the user's current location. The progression from location-awareness to location-prediction can enable the next generation of proactive, context-predicting applications.

Existing location-prediction algorithms predict someone's next location. In contrast, this dissertation predicts someone's future locations. Existing algorithms use a sequence of locations and predict the next location in the sequence. This dissertation incorporates temporal information as timestamps in order to predict someone's location at any time in the future. Sequence predictors based on Markov models have been shown to be effective predictors of someone's next location. This dissertation applies a Markov model to two-dimensional, timestamped location information to predict future locations.

This dissertation also predicts when someone will be at a given location. These predictions can support presence or understanding co-workers’ routines. Predicting the times that someone is going to be at a given location is a very different and more difficult problem than predicting where someone will be at a given time. A location-prediction application may predict one or two key locations for a given time, while there could be hundreds of correct predictions for times of the day that someone will be in a given location. The approach used in this dissertation, a heuristic model loosely based on Market Basket Analysis, is the first to predict when someone will arrive at any given location.

The models are applied to sparse, WiFi mobility data collected on PDAs given to 275 college freshmen. The location-prediction model predicts future locations with 78-91% accuracy. The temporal-prediction model achieves 33-39% accuracy. If a tolerance of plus/minus twenty minutes is allowed, the prediction rates rise to 77%-91%.

This dissertation shows the characteristics of the timestamped, location data which lead to the highest number of correct predictions. The best data cover large portions of the day, with less than three locations for any given timestamp.