HIGHLIGHTS
SUMMARY
The authors review the literature that is relevant to the study in three main streams: (i) differences and similarities between two main classes of temporal models, i.e., dynamic Bayesian networks (DBNs) and recurrent neural_networks (RNNs), (ii) the intersection of these two classes through recent recurrent latent variable models, and_(iii) state-of-the-art deep learning architectures for the task of urban mobility modeling. Advances 2 Pattern Recognition 129 108752 in deep learning architectures however, shifted this supremacy towards the field of Recurrent Neural_Networks (RNNs). RNNs and mixture density outputs Recurrent neural_networks are . . .
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