%0 Conference Paper %1 gambs2012next %A Gambs, Sebastien %A Killijian, Marc-Olivier %A del Prado Cortez, Miguel Nunez %B Workshop on Measurement, Privacy, and Mobility %C New York, NY, USA %D 2012 %I ACM %K chain citedby:scholar:count:111 citedby:scholar:timestamp:2017-2-4 diss geo inthesis markov next place prediction spatial %P 3:1--3:6 %R 10.1145/2181196.2181199 %T Next Place Prediction Using Mobility Markov Chains %U http://doi.acm.org/10.1145/2181196.2181199 %X In this paper, we address the issue of predicting the next location of an individual based on the observations of his mobility behavior over some period of time and the recent locations that he has visited. This work has several potential applications such as the evaluation of geo-privacy mechanisms, the development of location-based services anticipating the next movement of a user and the design of location-aware proactive resource migration. In a nutshell, we extend a mobility model called Mobility Markov Chain (MMC) in order to incorporate the n previous visited locations and we develop a novel algorithm for next location prediction based on this mobility model that we coined as n-MMC. The evaluation of the efficiency of our algorithm on three different datasets demonstrates an accuracy for the prediction of the next location in the range of 70\% to 95\% as soon as n = 2. %@ 978-1-4503-1163-2

@inproceedings{gambs2012next, abstract = {In this paper, we address the issue of predicting the next location of an individual based on the observations of his mobility behavior over some period of time and the recent locations that he has visited. This work has several potential applications such as the evaluation of geo-privacy mechanisms, the development of location-based services anticipating the next movement of a user and the design of location-aware proactive resource migration. In a nutshell, we extend a mobility model called Mobility Markov Chain (MMC) in order to incorporate the n previous visited locations and we develop a novel algorithm for next location prediction based on this mobility model that we coined as n-MMC. The evaluation of the efficiency of our algorithm on three different datasets demonstrates an accuracy for the prediction of the next location in the range of 70\% to 95\% as soon as n = 2.}, acmid = {2181199}, added-at = {2017-02-04T22:45:43.000+0100}, address = {New York, NY, USA}, articleno = {3}, author = {Gambs, Sebastien and Killijian, Marc-Olivier and del Prado Cortez, Miguel Nunez}, biburl = {https://www.bibsonomy.org/bibtex/2cea7c1290a7a6ed0431842d68d937ea9/becker}, booktitle = {Workshop on Measurement, Privacy, and Mobility}, doi = {10.1145/2181196.2181199}, interhash = {9537c320cc09454411eca7882a44031c}, intrahash = {cea7c1290a7a6ed0431842d68d937ea9}, isbn = {978-1-4503-1163-2}, keywords = {chain citedby:scholar:count:111 citedby:scholar:timestamp:2017-2-4 diss geo inthesis markov next place prediction spatial}, location = {Bern, Switzerland}, numpages = {6}, pages = {3:1--3:6}, publisher = {ACM}, series = {MPM '12}, timestamp = {2017-12-20T17:59:35.000+0100}, title = {Next Place Prediction Using Mobility Markov Chains}, url = {http://doi.acm.org/10.1145/2181196.2181199}, year = 2012 }