  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.
