Mobdatu: A New Model For Human Mobility Prediction Based On Heterogeneous Data

Several previous mobility models aim at describing or predicting human behavior in a particular region during a certain period of time. Nevertheless, most of those models have been evaluated using data from a single source, such as data from mobile calls or GPS data obtained from Web applications. Thus, the effectiveness of such models when using different types of data remains unknown. This paper proposes a new model to predict human mobility, called MobDatU, which was designed to use data from mobile calls and data from georeferenced applications (in an isolated or combined way). MobDatU as well as two state-of-the-art models, namely SMOOTH and Leap Graph, are evaluated considering various scenarios with single data source and multiple data sources. The experiments indicate that MobDatU always produces results that are better than or at least comparable to the best baseline in all scenarios, unlike the previous models whose performance is very dependent on the particular type of data used.