Clustering and prediction of mobile user routes from cellular data

Location-awareness and prediction of future locations is an important problem in pervasive and mobile computing. In cellular systems (e.g., GSM) the serving cell is easily available as an indication of the user location, without any additional hardware or network services. With this location data and other context variables we can determine places that are important to the user, such as work and home. We devise online algorithms that learn routes between important locations and predict the next location when the user is moving. We incrementally build clusters of cell sequences to represent physical routes. Predictions are based on destination probabilities derived from these clusters. Other context variables such as the current time can be integrated into the model. We evaluate the model with real location data, and show that it achieves good prediction accuracy with relatively little memory, making the algorithms suitable for online use in mobile environments.