Mobile Users Location Prediction with Complex Behavior Understanding

The growing ubiquity of smart-phones equipped with built-in sensors and global positioning system (GPS) has resulted in the collection of large volumes of mobility data without the need of any additional devices. The large size of heterogeneous mobility data gives rise to rapid development of location-based services (LBSs). The predictability of mobile users’ behavior is essential to enhance LBSs. To predict human mobility, many techniques have been proposed. However, existing techniques require good data quality to guarantee optimal performance. In this paper, we proposed a hybrid Markov chain to predict mobile users’ future locations. Our model constantly adapts to available user trace quality to select either the first order or the second order Markov chain. Compared to existing solutions, our model is adaptive to discrete gaps in data trace. In addition, we implemented a proper mechanism to predict congestion in city areas. To help us understanding complex user behaviors, we have also proposed a technique benefiting both temporal and spatial parameters to extract Zone of Interests (ZOIs). To evaluate the algorithms performance, we use a real-life dataset from the Nokia Mobile Data Challenge (MDC) collected around Lake Geneva region from 180 users. Experiment results show that our approach could achieve a good location prediction accuracy as well as area congestion prediction for most of the users