WhereNext: a location predictor on trajectory pattern mining

The pervasiveness of mobile devices and location based services is leading to an increasing volume of mobility data. This side effect provides the opportunity for innovative methods that analyse the behaviors of movements. In this paper we propose WhereNext, which is a method aimed at predicting with a certain level of accuracy the next location of a moving object. The prediction uses previously extracted movement patterns named Trajectory Patterns, which are a concise representation of behaviors of moving objects as sequences of regions frequently visited with a typical travel time. A decision tree, named T-pattern Tree, is built and evaluated with a formal training and test process. The tree is learned from the Trajectory Patterns that hold a certain area and it may be used as a predictor of the next location of a new trajectory finding the best matching path in the tree. Three different best matching methods to classify a new moving object are proposed and their impact on the qu...

• Type: Conference
• Year: 2009
• Where: KDD
• Authors: Anna Monreale, Fabio Pinelli, Roberto Trasarti, Fosca Giannotti
• Keywords: Certain Area, Data Mining, KDD 2009, Patterns Named Trajectory, Trajectory Patterns