Pedestrian-movement prediction based on mixed Markov-chain model

  title={Pedestrian-movement prediction based on mixed Markov-chain model},
  author={Akinori Asahara and Kishiko Maruyama and Akiko Sato and Kouichi Seto},
A method for predicting pedestrian movement on the basis of a mixed Markov-chain model (MMM) is proposed. MMM takes into account a pedestrian's personality as an unobservable parameter. It also takes into account the effects of the pedestrian's previous status. A promotional experiment in a major shopping mall demonstrated that the highest prediction accuracy of the MMM method is 74.4%. In comparison with methods based on a Markov-chain model (MM) and a hidden-Markov model (HMM) (i.e… CONTINUE READING
Highly Cited
This paper has 108 citations. REVIEW CITATIONS
Related Discussions
This paper has been referenced on Twitter 1 time. VIEW TWEETS

From This Paper

Topics from this paper.


Publications citing this paper.
Showing 1-10 of 64 extracted citations

Predicting Consumers' Locations in Dynamic Environments via 3D Sensor-Based Tracking

2014 Eighth International Conference on Next Generation Mobile Apps, Services and Technologies • 2014
View 6 Excerpts
Highly Influenced

Semantic Place Recognition for Context Aware Services

View 8 Excerpts
Highly Influenced

Scalable selective traffic congestion notification

MobiGIS • 2015
View 6 Excerpts
Highly Influenced

109 Citations

Citations per Year
Semantic Scholar estimates that this publication has 109 citations based on the available data.

See our FAQ for additional information.


Publications referenced by this paper.

IMES for mobile users. social implementation and experiments based on existing cellular phones for seamless positioning

D Manandhar, S Kawaguchi, M Uchida, M Ishii, H Tomohiro
Proc. of the Int. Symposium on GPS/GNSS 2008 • 2008
View 8 Excerpts
Highly Influenced

Similar Papers

Loading similar papers…