A mixed autoregressive hidden-markov-chain model applied to people's movements

Authors:
Akinori Asahara;Kishiko Maruyama;Ryosuke Shibasaki
Affiliations:
Hitachi Ltd., Kokubunji-shi, Tokyo, Japan;Hitachi Ltd., Kokubunji-shi, Tokyo, Japan;University of Tokyo, Kashiwa-shi, Chiba, Japan
Venue:
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
Year:
2012

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Abstract

A "mixed autoregressive hidden Markov model" (MAR-HMM) is proposed for modeling people's movements. MAR-HMM is equivalent to a special case of an autoregressive hidden Markov model (AR-HMM), which takes into account changes of people's internal properties. The number of parameters is thus reduced in the case of MAR-HMM. A dataset is applied to evaluate MAR-HMM in this study. The prediction rate of MAR-HMM is 56.8% and that of AR-HMM is 51.5%. It is therefore concluded that MAR-HMM is applicable to trajectory analysis of pedestrians.