A Systematic Analysis of Fine-Grained Human Mobility Prediction with On-Device Contextual Data

User mobility prediction is widely considered to be helpful for various sorts of location based services on mobile devices. A large amount of studies have explored different algorithms to predict where a user will visit in the future based on their current and historical contexts and trajectories. Most of them focus on specific targets of predictions, such as the next venue a user checks in or the destination of her next trip, which usually depend on what their task is and what is available in their data. While successful stories are often reported, little discussion can be found on what happens if the prediction targets vary: whether coarser locations are easier to be predicted than finer locations, and whether predicting the immediate next location on the trajectory is easier than predicting the destination. On the other hand, commonly used in these prediction tasks, few have utilized finer grained, on-device user behavioral data, which are supposed to be indicative of user intentions. In this paper, we conduct a systematic study on the problem of mobility prediction using a fine-grained real-world dataset. Based on a Markov model, a recurrent neural network, and a multi-modal learning method, we perform a series of experiments to investigate the predictability of different types of granularities of prediction targets and the effectiveness of different types of signals. The results provide many insights on what can be predicted and how, which sheds light on real-world mobility prediction in general.