Learning human mobility patterns by learning a hierarchical hidden Markov model using latent Dirichlet allocation Eyal Ben Zion , Boaz Lerner Department of Industrial and Management Engineering, Ben-Gurion University of the Negev We wish to learn, and infer on, a human lifestyle from their location records We suggest a three-layer latent variable model with layers for stop areas, trajectories, and lifestyles We use a dynamic Bayesian network (DBN) implementation of a hierarchical hidden Markov model We learn the cardinalities of the latent variables using hierarchical clustering and latent Dirichlet allocation (LDA) Our algorithm is much faster than existing algorithms also providing a better fit to the data