Probabilistic Graphical Model based Personal Route Prediction in Mobile Environment
Jemin Kim, Haejung Baek, Young-Tack Park · 2012
Abstract: Individuals tend to follow their own preferred paths when traveling to specific places. Information on these routes could be utilized to build various intelligent LBSs. In order to predict a current user’s route, various approaches have been researched. In this paper, we suggest a practical approach to learning users ' route patterns from their histories and using that information to predict specific routes. In cases where existing routes overlap, i.e., where parts of routes are the same, in a user's route model, it is difficult to identify the user's intended path. For more accurate prediction, firstly, we extract route patterns by adopting image processing. Secondly, we build a state-observation model reflecting users ' intentions, based on route patterns, temporal features and weather information. Our approach consist of four steps: recognizing regions for splitting routes into trip segments, route pattern mining, learning users ' route models and trip route prediction. Our method achieved a prediction accuracy of 96.4 % in tests performed with 15 smartphone users.