NexT: A framework for next-place prediction on location based social networks

The extensive use of location-based social networks (LBSNs) allows for the collection of huge amount of geo-tagged data about people activities and costumes within urban context, including human mobility regularities. In this context, predicting the future position of a mobile object is the key for the implementations of several applications aiming at improving mobility within urban areas (e.g., traffic congestion, location-based advertisements). The paper proposes NexT, a next-place prediction framework, which exploits LBSNs data to forecast the next location of an individual based on the observations of her mobility behavior over some period of time and the recent locations that she has visited (individuals typical mobility routines) and on global mobility in the considered geographic area (e.g., mobility routines of all the Twitter users). The approach integrates frequent pattern mining and feature-based supervised classification, exploiting a set of spatio-temporal features characterizing locations and movements among them. The features are combined into a decision tree prediction model. The experimental evaluation, performed on real-world tweets shows the effectiveness and efficiency of the approach in predicting users next places, achieving a remarkable accuracy and prediction rate, outperforming state-of-the art approaches.