WhereNext

A WhereNext is an algorithm that predicts the next location of moving object based on its previous trajectory patterns.

• See: Prediction Task, Trajectory, Moving Object, Decision Tree.

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References

2009

• (Monreale et al., 2009) ⇒ Anna Monreale, Fabio Pinelli, Roberto Trasarti, and Fosca Giannotti. (2009). “WhereNext: A Location Predictor on Trajectory Pattern Mining.” In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2009). doi:10.1145/1557019.1557091
  • QUOTE: 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 quality of prediction is studied extensively. Using Trajectory Patterns as predictive rules has the following implications : (I) the learning depends on the movement of all available objects in a certain area instead of on the individual history of an object; (II) the prediction tree intrinsically contains the spatio-temporal properties that have emerged from the data and this allows us to define matching methods that striclty depend on the properties of such movements.

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