MSSRM: A Multi-Embedding Based Self-Attention Spatio-temporal Recurrent Model for Human Mobility Prediction

초록

Human mobility affects many aspects of an urban area, including spatial structure, temporal connectivity, even response to epidemics. Prediction of human mobility is of great significance for a wide spectrum of location-based applications. To enhance the spatio-temporal contexts between check-ins, we encode check-in locations as a graph and propose a multi-embedding based self-attention spatio-temporal recurrent model (MSSRM) for human mobility prediction. In this paper, we first obtain elaborate spatial and temporal embeddings from the directed weighted graph of spatio-temporal points and the frequency distribution of users' visits. Subsequently, we adopt a long short-term memory layer to capture the long-term and short-term spatio-temporal dependencies and introduce a self-attention mechanism to distinguish each location in different contexts. Finally, we use a fully connected layer and incorporate user information to yield prediction results. Extensive experiment results based on two real-world datasets demonstrate that our model outperforms the state-of-the-art models.

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