%0 Conference Paper %1 asahara2011pedestrianmovement %A Asahara, Akinori %A Maruyama, Kishiko %A Sato, Akiko %A Seto, Kouichi %B International Conference on Advances in Geographic Information Systems (SIGSPATIAL) %C New York, NY, USA %D 2011 %I ACM %K chain diss inthesis markov mixed mixedtrails mobility model movement navigation pedestrian %P 25--33 %R 10.1145/2093973.2093979 %T Pedestrian-movement Prediction Based on Mixed Markov-chain Model %U http://doi.acm.org/10.1145/2093973.2093979 %X 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., prediction rates of about 45\% and 2\%, respectively), the proposed MMM-based prediction method is substantially more accurate. This pedestrian-movement prediction based on MMM using tracking data will make it possible to provide so-called ädaptive mobile services" with proactive functions. %@ 978-1-4503-1031-4

@inproceedings{asahara2011pedestrianmovement, abstract = {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., prediction rates of about 45\% and 2\%, respectively), the proposed MMM-based prediction method is substantially more accurate. This pedestrian-movement prediction based on MMM using tracking data will make it possible to provide so-called "adaptive mobile services" with proactive functions.}, acmid = {2093979}, added-at = {2016-12-18T12:12:25.000+0100}, address = {New York, NY, USA}, author = {Asahara, Akinori and Maruyama, Kishiko and Sato, Akiko and Seto, Kouichi}, biburl = {https://www.bibsonomy.org/bibtex/2ec1bcaafe56d7066da0d8cfc641bad0f/becker}, booktitle = {International Conference on Advances in Geographic Information Systems (SIGSPATIAL)}, doi = {10.1145/2093973.2093979}, interhash = {58a06d2c349640ceefa838fc2b042331}, intrahash = {ec1bcaafe56d7066da0d8cfc641bad0f}, isbn = {978-1-4503-1031-4}, keywords = {chain diss inthesis markov mixed mixedtrails mobility model movement navigation pedestrian}, location = {Chicago, Illinois}, numpages = {9}, pages = {25--33}, publisher = {ACM}, series = {GIS '11}, timestamp = {2017-12-20T14:06:31.000+0100}, title = {Pedestrian-movement Prediction Based on Mixed Markov-chain Model}, url = {http://doi.acm.org/10.1145/2093973.2093979}, year = 2011 }