Spatial and temporal considerations in next place predictions

We present synthesized findings from a systematic study of user mobility based on a well grounded data set through mining attributes of place-to-place transitions. Next place predictions are the atomic units in constructing end-to-end user mobility trajectories based on historical trace data. These trajectories in turn form models for opportunistic networks to be utilized for providing location and communication services. We start with a baseline of the user's current place, start time, and end time to predict the next place. We demonstrate the efficiency of an algorithm called PeriodicaS through aggregated average prediction accuracies across all users over a large set of diverse participants. PeriodicaS mines periodicity intelligently in users' mobility traces and further improves prediction accuracies with additional classification rules. We derive these classification rules by applying explicit semantic annotations (home, work place and public transportation points associated with places visited), and accompanying group information. We propose novel ways of transforming bits of information in the mobility traces, defined to be inherent semantic annotations, as features for mobility modeling in PeriodicaS. Inherent semantic annotations are computed with temporal variations from visited places such as end time only, and measuring duration time. We deduce more inherent semantic annotations from place rankings by frequency of visits. By progressively employing these two types of semantic annotations, explicitly stated in the data set and deduced from the mobility traces, we improve next place prediction accuracies up to 54% compared to baseline predictions.