We introduce a Markov Modulated Process (MMP) to describe human mobility. We represent the mobility process as a time-varying graph, where a link specifies a connection between two nodes (humans) at any discrete time step. Each state of the Markov chain encodes a certain modification to the original graph. We show that our MMP model successfully captures the main features of a random mobility simulator, in which nodes moves in a square region. We apply our MMP model to human mobility, measured in a library.
Markov Modulated Process to Model Human Mobility / Chang, B.; Yang, L.; Sensi, M.; Achterberg, M. A.; Wang, F.; Rinaldi, M.; Mieghem, P. V.. - 1015:(2022), pp. 607-618. (Intervento presentato al convegno 10th International Conference on Complex Networks and Their Applications, COMPLEX NETWORKS 2021 tenutosi a Madrid (Esp) nel 30 November 2021 through 2 December 2021) [10.1007/978-3-030-93409-5_50].
Markov Modulated Process to Model Human Mobility
Sensi M.;
2022
Abstract
We introduce a Markov Modulated Process (MMP) to describe human mobility. We represent the mobility process as a time-varying graph, where a link specifies a connection between two nodes (humans) at any discrete time step. Each state of the Markov chain encodes a certain modification to the original graph. We show that our MMP model successfully captures the main features of a random mobility simulator, in which nodes moves in a square region. We apply our MMP model to human mobility, measured in a library.File | Dimensione | Formato | |
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MMP_for_Human_Mobility.pdf
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MMP_human_mobility_review.pdf
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https://hdl.handle.net/11583/2993448