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General Information
Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2014 Vol.4(4): 319-327 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2014.V4.431

Hidden Markov Flow Network Model: A Generative Model for Dynamic Flow on a Network

Satoshi Koide, Hiroshi Ohno, Ryuta Terashima, Thanomsak Ajjanapanya, and Itti Rittaporn
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: koide@mosk.tytlabs.co.jp, oono-h@mosk.tytlabs.co.jp, ryuta@mosk.tytlabs.co.jp ).
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).

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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.

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