ABSTRACT
The ability to foresee the next moves of a user is crucial to ubiquitous computing. Disregarding major differences in individuals' routines, recent ground-breaking analysis on mobile phone data suggests high predictability in mobility. By nature, however, mobile phone data offer very low spatial and temporal resolutions. It remains largely unknown how the predictability changes with respect to different spatial/temporal scales. Using high-resolution GPS data, this paper investigates the scaling effects on predictability. Given specified spatial-temporal scales, recorded trajectories are encoded into long strings of distinct locations, and several information-theoretic measures of predictability are derived. Somewhat surprisingly, high predictability is still present at very high spatial/temporal resolutions. Moreover, the predictability is independent of the overall mobility area covered. This suggests highly regular mobility behaviors. Moreover, by varying the scales over a wide range, an invariance is observed which suggests that certain trade-offs between the predicting accuracy and spatial-temporal resolution are unavoidable. As many applications in ubiquitous computing concern mobility, these findings should have direct implications.
- D. Ashbrook and T. Starner. Using GPS to learn significant locations and predict movement across multiple users. Personal Ubiquitous Comput., 7(5):275--286, 2003. Google ScholarDigital Library
- A. Bhattacharya and S. K. Das. Lezi-update: an information-theoretic approach to track mobile users in pcs networks. MobiCom, pages 1--12, 1999. Google ScholarDigital Library
- H. Fang, W.-J. Hsu, and L. Rudolph. Cognitive personal positioning based on activity map and adaptive particle filter. In MSWiM, pages 405--412, 2009. Google ScholarDigital Library
- M. Feder, N. Merhav, and M. Gutman. Universal prediction of individual sequences. Information Theory, IEEE Transactions on, 38(4):1258--1270, 1992. Google ScholarDigital Library
- M. C. González, C. A. Hidalgo, and A.-L. Barabási. Understanding individual human mobility patterns. Nature, 453(7196):779--782, 2008.Google ScholarCross Ref
- R. Hariharan and K. Toyama. Project lachesis: parsing and modeling location histories. In GIS, pages 106--124, 2004.Google Scholar
- M. W. Horner and M. E. O'Kelly. Embedding economies of scale concepts for hub network design. Journal of Transport Geography, 9(4):255--265, 2001.Google ScholarCross Ref
- B. Jensen, J. Larsen, K. Jensen, J. Larsen, and L. Hansen. Estimating human predictability from mobile sensor data. In MLSP, pages 196--201, 2010.Google Scholar
- H. Jeung, Q. Liu, H. T. Shen, and X. Zhou. A hybrid prediction model for moving objects. ICDE, pages 70--79, 2008. Google ScholarDigital Library
- H. Jeung, H. T. Shen, and X. Zhou. Mining trajectory patterns using hidden markov models. DaWak, pages 470--480, 2007. Google ScholarDigital Library
- J. Kleinberg. The wireless epidemic. Nature, 449:287--288, 2007.Google ScholarCross Ref
- I. Kontoyiannis, P. Algoet, Y. Suhov, and A. Wyner. Nonparametric entropy estimation for stationary processes and random fields, with applications to English text. Information Theory, IEEE Transactions on, 44(3):1319--1327, 1998. Google ScholarDigital Library
- J. Krumm and E. Horvitz. Predestination: Inferring destinations from partial trajectories. In UbiComp, pages 243--260, 2006. Google ScholarDigital Library
- C. Song, Z. Qu, N. Blumm, and A.-L. Barabái. Limits of predictability in human mobility. Science, 327:1018--1021, 2010.Google ScholarCross Ref
- Y. Zheng, Q. Li, Y. Chen, X. Xie, and W.-Y. Ma. Understanding mobility based on gps data. In Proc. UbiComp, pages 312--321, 2008. Google ScholarDigital Library
- Y. Zheng, L. Zhang, X. Xie, and W.-Y. Ma. Mining interesting locations and travel sequences from gps trajectories. In Proc. WWW, pages 791--800, 2009. Google ScholarDigital Library
Index Terms
- Predictability of individuals' mobility with high-resolution positioning data
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