  Human mobility prediction is a core functionality in many location-based
services and applications. However, due to the sparsity of mobility data, it is
not an easy task to predict future POIs (place-of-interests) that are going to
be visited. In this paper, we propose MobTCast, a Transformer-based
context-aware network for mobility prediction. Specifically, we explore the
influence of four types of context in the mobility prediction: temporal,
semantic, social and geographical contexts. We first design a base mobility
feature extractor using the Transformer architecture, which takes both the
history POI sequence and the semantic information as input. It handles both the
temporal and semantic contexts. Based on the base extractor and the social
connections of a user, we employ a self-attention module to model the influence
of the social context. Furthermore, unlike existing methods, we introduce a
location prediction branch in MobTCast as an auxiliary task to model the
geographical context and predict the next location. Intuitively, the
geographical distance between the location of the predicted POI and the
predicted location from the auxiliary branch should be as close as possible. To
reflect this relation, we design a consistency loss to further improve the POI
prediction performance. In our experimental results, MobTCast outperforms other
state-of-the-art next POI prediction methods. Our approach illustrates the
value of including different types of context in next POI prediction.
