ABSTRACT
Models of human mobility have broad applicability in fields such as mobile computing, urban planning, and ecology. This paper proposes and evaluates WHERE, a novel approach to modeling how large populations move within different metropolitan areas. WHERE takes as input spatial and temporal probability distributions drawn from empirical data, such as Call Detail Records (CDRs) from a cellular telephone network, and produces synthetic CDRs for a synthetic population. We have validated WHERE against billions of anonymous location samples for hundreds of thousands of phones in the New York and Los Angeles metropolitan areas. We found that WHERE offers significantly higher fidelity than other modeling approaches. For example, daily range of travel statistics fall within one mile of their true values, an improvement of more than 14 times over a Weighted Random Waypoint model. Our modeling techniques and synthetic CDRs can be applied to a wide range of problems while avoiding many of the privacy concerns surrounding real CDRs.
- S. Almeida, J. Queijo, and L. Correia. Spatial and temporal traffic distribution models for gsm. In Vehicular Technology Conference, Sept. 1999.Google ScholarCross Ref
- D. Applegate, T. Dasu, S. Krishnan, and S. Urbanek. Unsupervised clustering of multidimensional distributions using earth mover distance. In Proc. KDD'11, 2011. Google ScholarDigital Library
- M. A. Bayir, M. Demirbas, and N. Eagle. Discovering spatiotemporal mobility profiles of cellphone users. World of Wireless, Mobile and Multimedia Networks and Workshops, 2009.Google Scholar
- R. Becker, R. Cáceres, K. Hanson, J. M. Loh, S. Urbanek, A. Varshavsky, and C. Volinsky. Route classification using cellular handoff patterns. In 13th International Conference on Ubiquitous Computing (Ubicomp), Sept. 2011. Google ScholarDigital Library
- J. Candia, M. C. González, P. Wang, T. Schoenharl, G.Madey, and A.-L. Barabási. Uncovering individual and collective human dynamics from mobile phone records. MATH.THEOR., 41:224015, 2008.Google ScholarCross Ref
- K. Dufková, J.-Y. Le Boudec, L. Kencl, and M. Bjelica. Predicting user-cell association in cellular networks from tracked data. Intl. workshop on Mobile Entity Localization and Tracking in GPS-less Environnments, 2009. Google ScholarDigital Library
- C. Dwork. Differential privacy: A survey of results. In Theory and Applications of Models of Computation (TAMC). Springer Verlag, April 2009. Google ScholarDigital Library
- K. Fall. A delay-tolerant network architecture for challenged internets. In SIGCOMM, 2003. Google ScholarDigital Library
- F. Girardin, F. Calabrese, F. Dal Fiorre, A. Biderman, C. Ratti, and J. Blat. Uncovering the presence and movements of tourists from user-generated content. In Intn'l Forum on Tourism Statistics, 2008.Google Scholar
- F. Girardin, F. Dal Fiore, J. Blat, and C. Ratti. Understanding of tourist dynamics from explicitly disclosed location information. Symposium on LBS and Telecartography, 2007.Google Scholar
- F. Girardin, A. Vaccari, A. Gerber, A. Biderman, and C. Ratti. Towards estimating the presence of visitors from the aggregate mobile phone network activity they generate. In Intl. Conference on Computers in Urban Planning and Urban Management, 2009.Google Scholar
- P. Golle and K. Partridge. On the anonymity of home/work location pairs. In Seventh International Conference on Pervasive Computing (Pervasive 2009), 2009. Google ScholarDigital Library
- M. C. González, C. A. Hidalgo, and A.-L. Barabási. Understanding individual human mobility patterns. Nature, 453, 2008.Google Scholar
- Google Maps. http://www.census.gov.Google Scholar
- W.-J. Hsu, T. Spyropoulos, K. Psounis, and A. Helmy. Modeling time-variant user mobility in wireless mobile networks. In IEEE INFOCOM, 2007.Google ScholarDigital Library
- S. Isaacman, R. Becker, R. Cáceres, S. Kobourov, M. Martonosi, J. Rowland, and A. Varshavsky. Identifying important places in people's lives from cellular network data. In 9th International Conf. on Pervasive Computing, 2011. Google ScholarDigital Library
- S. Isaacman, R. Becker, R. Cáceres, S. Kobourov, M. Martonosi, J. Rowland, and A. Varshavsky. Ranges of human mobility in los angeles and new york. In Eighth IEEE Workshop on Managing Ubiquitous Communications and Services, 2011.Google ScholarCross Ref
- S. Isaacman, R. Becker, R. Cáceres, S. Kobourov, J. Rowland, and A. Varshavsky. A tale of two cities. In Workshop on Mobile Computing Systems and Applications (HotMobile), 2010. Google ScholarDigital Library
- D. B. Johnson and D. A. Maltz. Dynamic source routing in ad hoc wireless networks. In Mobile Computing, pages 153--181. Kluwer Academic Publishers, 1996.Google ScholarCross Ref
- M. Kim, D. Kotz, and S. Kim. Extracting a mobility model from real user traces. In IEEE INFOCOM, 2006.Google ScholarCross Ref
- J. Krumm. Inference attacks on location tracks. In Fifth International Conference on Pervasive Computing (Pervasive 2007), 2007. Google ScholarDigital Library
- K. Laasonen. Mining Cell Transition Data. PhD thesis, University of Helsinki, Finland, 2009.Google Scholar
- F. McSherry and R. Mahajan. Differentially-private network trace analysis. In Proc. ACM SIGCOMM, 2010. Google ScholarDigital Library
- W. Navidi and T. Camp. Stationary distributions for random waypoint models. IEEE Transactions on Mobile Computing, 3(1):99--108, 2004. Google ScholarDigital Library
- A. Noulas, S. Scellato, R. Lambiotte, M. Pontil, and C. Mascolo. A tale of many cities: universal patterns in human urban mobility. PLoS ONE, 2012.Google Scholar
- O. Pele and M. Werman. Fast and robust earth mover's distances. In ICCV, 2009.Google ScholarCross Ref
- D. Pelleg and A. Moore. X-means: Extending k-means with efficient estimation of the number of clusters. In 17th International Conf. on Machine Learning, 2000. Google ScholarDigital Library
- I. Rhee, M. Shin, S. Hong, K. Lee, and S. Chong. On the levy-walk nature of human mobility: Do humans walk like monkeys? In IEEE INFOCOM, 2008.Google Scholar
- Y. Rubner, C. Tomasi, and L. J. Guibas. A metric for distributions with applications to image databases. In Proc. IEEE International Conference on Computer Vision, 1998. Google ScholarDigital Library
- Y. Rubner, C. Tomasi, and L. J. Guibas. The earth mover's distance as a metric for image retrieval. International Journal of Computer Vision, 40, 2000. Google ScholarDigital Library
- C. Song, Z. Qu, N. Blumm, and A.-L. Barabási. Limits of predictability in human mobility. Science, 327, 2010.Google Scholar
- US Census Bureau. http://www.census.gov.Google Scholar
- A. Vahdat and D. Becker. Epidemic routing for partially connected ad hoc networks. Technical Report CS-200006, Duke University, 2000.Google Scholar
- J. Yoon, B. Noble, M. Liu, and M. Kim. Building realistic mobility models from coarse-grain traces. In Proc. ACM MobiSys, 2006. Google ScholarDigital Library
- H. Zang and J. Bolot. Anonymization of location data does not work: A large-scale measurement study. In Seventeenth Annual International Conference on Mobile Computing and Networking (MobiCom 2011), 2011. Google ScholarDigital Library
- G. Zyba, G. M. Voelker, S. Ioannidis, and C. Diot. Dissemination in opportunistic mobile ad-hoc networks: The power of the crowd. In IEEE INFOCOMM, 2011.Google ScholarCross Ref
Index Terms
- Human mobility modeling at metropolitan scales
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