Mobile crowd location prediction with hybrid features using ensemble learning

With the explosive growth of location-based service on mobile devices, predicting users’ future locations and trajectories is of increasing importance to support proactive information services. In this paper, we model this problem as a supervised learning task and propose to use ensemble learning methods with hybrid features to solve it. We characterize the properties of users’ visited locations and movement patterns and then extract feature types (temporal, spatial, and system) to quantify the correlation between locations and features. Finally, we apply ensemble methods to predict users’ future locations with extracted features. Moreover, we design an adaptive Markov Chain model to predict users’ trajectories between two locations. To evaluate the system performance, we use a real-life dataset from the Nokia Mobile Data Challenge. Experiment results unveil interesting findings: (1) For individual predictors, Bayes Networks outperform all others when data quality is good, while J48 delivers the best results when data quality is bad; (2) Ensemble predictors outperform individual predictors in general under all conditions; and (3) Ensemble predictor performance depends on the user movement patterns.