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.
- L. O. Alvares, V. Bogorny, B. Kuijpers, B. Moelans, J. A. Fern, E. D. Macedo, and A. T. Palma. Towards semantic trajectory knowledge discovery. Technical Report, Hasselt University, Limbourg, Belgium, 2007.Google Scholar
- A. Asahara, A. Sato, K. Maruyama, and K. Seto. Pedestrian-movement prediction based on mixed Markov-chain model. In Proceedings of the 19th International Conference on Advances in Geographic Information Systems, pages 25--33, IL, USA, 2011. Google ScholarDigital Library
- D. Ashbrook and T. Starner. Learning significant locations and predicting user movement with GPS. In Proceedings of the 6th International Symposium on Wearable Computers, pages 275--286, Sardina, Italy, 2003. Google ScholarDigital Library
- J. Froehlich and J. Krumm. Route prediction from trip observations. In Proceedings of the Society of Automotive Engineers World Congress, MI, USA, 2008.Google ScholarCross Ref
- S. Gambs, M.-O. Killijian, and M. Nuñez del Prado C. Show me how you move and I will tell you who you are. Transactions on Data Privacy, volume 2:103--126, Catalonia, Spain, 2011. Google ScholarDigital Library
- M. Killijian, M. Roy, and G. Tredan. Beyond San Francisco cabs: Building a *-lity mining dataset. In Proceedings of the Workshop on the Analysis of Mobile Phone Networks, pages 75--78, Cambridge, MA, USA, 2010.Google Scholar
- J. Krumm and E. Horvitz. Predestination: Inferring destinations from partial trajectories. In Proceedings of the 8th International Conference on Ubiquitous Computing, pages 243--260,CA, USA, 2006. Google ScholarDigital Library
- A. Meyerson and R. Williams. On the complexity of optimal k-anonymity. In Proceedings of the 23rd Symposium on Principles of Database Systems, pages 223--228, NY, USA, 2004. Google ScholarDigital Library
- J. Petzold, F. Bagci, W. Trumler, and T. Ungerer. Comparison of different methods for next location prediction. In Proceedings of the 12th International Euro-Par Conference, pages 909--918, Berlin, Germany, 2006. Google ScholarDigital Library
- C. Song, Z. Qu, N. Blumm, and A.-L. Barabasi. Limits of predictability in human mobility. Science, volume 327:1018--1021, LA, USA, 2010.Google ScholarCross Ref
- L. Song, D. Kotz, R. Jain, and X. He. Evaluating next-cell predictors with extensive wi-fi mobility data. IEEE Transactions on Mobile Computing, volume 5:1633--1649, CA, USA, 2006. Google ScholarDigital Library
- J. S. Vitter and P. Krishnan. Optimal prefetching via data compression. Journal of the ACM, volume 43:771--793, NY, USA, 1996. Google ScholarDigital Library
- J. J.-C. Ying, W.-C. Lee, and T.-C. Weng. Semantic trajectory mining for location prediction. In Proceedings of the 19th International Conference on Advances in Geographic Information Systems, pages 34--43, NY, USA, 2011. Google ScholarDigital Library
- J. J.-C. Ying, E. H.-C. Lu, W.-C. Lee, T.-C. Weng, and V. S. Tseng. Mining user similarity from semantic trajectories. In Proceedings of the 2nd International Workshop on Location Based Social Networks, pages 19--26, NY, USA, 2010. Google ScholarDigital Library
- Y Zheng, Q. Li, Y. Chen, X. Xie, and Wei-Ying Ma. Understanding mobility based on GPS data. In Proceedings of the 10th International Conference on Ubiquitous Computing, pages 312--321, Seoul, Korea, 2008. Google ScholarDigital Library
- C. Zhou, D. Frankowski, P. J. Ludford, S. Shekhar, and L. G. Terveen. Discovering personal gazetters: an interactive clustering approach. In Proceedings of the 12th ACM International Workshop on Geographic Information Systems, pages 266--273, DC, USA, 2004. Google ScholarDigital Library
Index Terms
- Next place prediction using mobility Markov chains
Recommendations
An enhanced fast handover with seamless mobility support for next-generation wireless networks
To allow mobile node be always connected regardless of its location on the Internet, mobile IPv6 (MIPv6) is designed for next-generation wireless networks. However, this protocol has some inherent drawbacks: long handoff delay and high packet loss; ...
Markov mobility model and registration area optimization in cellular networks
In cellular communication systems, in order for a network to keep track of inactive mobile stations (MSs), each inactive MS has to update its location from time to time, called location registration. To lighten the task of tracking inactive MSs, the ...
Next Place Prediction: A Systematic Literature Review
PredictGIS 2018: Proceedings of the 2nd ACM SIGSPATIAL Workshop on Prediction of Human MobilityIn this systematic literature review an overview of the recent developments in the field of Next Place Prediction is given. Next Place Prediction in this work refers to the prediction of where an individual human will go to next, based on continuous ...
Comments