Abstract—In this paper, we propose a generative model that
describes the dynamics of flow on a network — the hidden
Markov flow network (HMFN) model, which is inspired by the
gravity model in traffic engineering. Each node in the network
has a dynamic hidden state and the flow observed on links
depends on the states of the nodes being connected. For model
inference, a collapsed Gibbs sampling algorithm is also
proposed. Lastly, the model is applied to synthetic data and real
human mobility network generated by GPS data from taxis in
Bangkok. The synthetic data example shows that the
reconstruction accuracy of the proposed method outperforms
compared with the k-means method and the hidden Markov
model, which do not consider the network interaction. The
results of human mobility data show that the HMFN model can
be used for spatio-temporal anomaly detection and prediction
of future flow patterns.
Index Terms—Generative model, dynamics of flow network, bayesian inference, spatio-temporal pattern mining.
Satoshi Koide, Hiroshi Ohno, and Ryuta Terashima are with Toyota Central R&D Laboratories, Nagakute, Aichi 480-1192, Japan (e-mail: email@example.com, firstname.lastname@example.org, email@example.com ).
Thanomsak Ajjanapanya and Itti Rittaporn are with Toyota Tsusho Electronics (Thailand) Co., Ltd., Pathumwan, Bangkok 10330, Thailand (e-mail: thanomsak@ ttet.co.th, itti@ ttet.co.th).
Cite: Satoshi Koide, Hiroshi Ohno, Ryuta Terashima, Thanomsak Ajjanapanya, and Itti Rittaporn, "Hidden Markov Flow Network Model: A Generative Model for Dynamic Flow on a Network," International Journal of Machine Learning and Computing vol.4, no. 4, pp. 319-327, 2014.