Next place prediction using mobility Markov chains

By Sébastien Gambs, Marc-Olivier Killijian, Miguel Núñez del Prado Cortez. MPM '12, 2012

606 citations recorded by the source corpus.

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A Survey on Trajectory-Prediction Methods for Autonomous Driving (2022, methodology): “HMM is represented by (S,O,A,B, π) [50], as shown in Fig.”
Understanding Human Mobility from Twitter (2014, background): “Another important measure for predictability is the maximum bound of probabilityP (or the maximum predictability) that an appropriate prediction algorithm [37] can predict the user’s next location.”
Approaching the Limit of Predictability in Human Mobility (2013, background): “2) model was found to not bring important improvement at the cost of a significant overhead in terms of computation and space for the learning and storing of the mobility model 9,12 .”
A Survey on Deep Learning for Human Mobility (2020, background): “Another peculiarity of DeepSTN+ is that it uses temporal factors and the distribution of POIs to gain prior knowledge of the crowd flows.”
Predicting future locations with hidden Markov models (2012, methodology): “extended a previously proposed mobility model called the Mobility Markov Chain (MMC), in order to consider the n previous visited locations [7].”