Far out: Predicting long-term human mobility

Proceedings of the National Conference on Artificial Intelligence

Volumn 1, Issue , 2012, Pages 814-820

  Sadilek, Adam ^a   Krumm, John ^b  

^a University of Rochester   (United States)

^b MICROSOFT RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CONTEXTUAL FEATURE; DATA SETS; EIGEN DECOMPOSITION; GPS DATA; HUMAN MOBILITY; LOCATION DATA; NONPARAMETRIC METHODS; RESEARCH AND APPLICATION; ROBUST PATTERNS;

ARTIFICIAL INTELLIGENCE;

FORECASTING;

EID: 84868278154     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (23)

1. Ashbrook, D., and Starner, T. 2003. Using GPS to learn significant locations and predict movement across multiple users. Personal Ubiquitous Comput. 7:275-286.
2. Ben-Israel, A., and Greville, T. 2003. Generalized inverses: theory and applications, volume 15. Springer Verlag.
3. Bettencourt, L., and West, G. 2010. A unified theory of urban living. Nature 467(7318):912-913.
4. Brigham, E., and Morrow, R. 1967. The fast Fourier transform. Spectrum, IEEE 4(12):63-70.
5. Cho, E.; Myers, S. A.; and Leskovec, J. 2011. Friendship and mobility: User movement in location-based social networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD).
6. Eagle, N., and Pentland, A. 2009. Eigenbehaviors: Identifying structure in routine. Behavioral Ecology and Sociobiology 63(7):1057-1066.
7. Gonzalez, M.; Hidalgo, C.; and Barabási, A. 2008. Understanding individual human mobility patterns. Nature 453(7196):779-782.
8. Jeung, H.; Liu, Q.; Shen, H.; and Zhou, X. 2008. A hybrid prediction model for moving objects. In Data Engineering, 2008. ICDE 2008. IEEE 24th International Conference on, 70-79. IEEE.
9. Jolliffe, I. 2002. Principal component analysis. Encyclopedia of Statistics in Behavioral Science.
10. Kim, M., and Kotz, D. 2011. Identifying unusual days. Journal of Computing Science and Engineering 5(1):71-84.
11. Kim, M.; Kotz, D.; and Kim, S. 2006. Extracting a mobility model from real user traces. In Proc. IEEE Infocom, 1-13. Citeseer.
12. Krumm, J., and Brush, A. 2011. Learning time-based presence probabilities. Pervasive Computing 79-96.
13. Krumm, J., and Horvitz, E. 2006. Predestination: Inferring destinations from partial trajectories. UbiComp 2006: Ubiquitous Computing 243-260.
14. Lee, K.; Hong, S.; Kim, S.; Rhee, I.; and Chong, S. 2009. Slaw: A new mobility model for human walks. In INFOCOM 2009, IEEE, 855-863. IEEE.
15. Li, Z.; Ding, B.; Han, J.; Kays, R.; and Nye, P. 2010. Mining periodic behaviors for moving objects. In Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, 1099-1108. ACM.
16. Liao, L.; Fox, D.; and Kautz, H. 2005. Location-based activity recognition using relational Markov networks. In IJCAI.
17. Musolesi, M., and Mascolo, C. 2009. Mobility models for systems evaluation, a survey.
18. Penrose, R. 1956. On best approximate solutions of linear matrix equations. In Proceedings of the Cambridge Philosophical Society, volume 52, 17-19. Cambridge Univ Press.
19. Sadilek, A., and Kautz, H. 2010. Recognizing multi-agent activities from GPS data. In Twenty-Fourth AAAI Conference on Artificial Intelligence.
20. Sadilek, A.; Kautz, H.; and Bigham, J. P. 2012. Finding your friends and following them to where you are. In Fifth ACM International Conference on Web Search and Data Mining. (Best Paper Award).
21. Scellato, S.; Musolesi, M.; Mascolo, C.; Latora, V.; and Campbell, A. 2011. Nextplace: A spatio-temporal prediction framework for pervasive systems. Pervasive Computing 152-169.
22. Tipping, M., and Bishop, C. 1999. Probabilistic principal component analysis. Journal of the Royal Statistical Society. Series B, Statistical Methodology 611-622.
23. Ziebart, B.; Maas, A.; Dey, A.; and Bagnell, J. 2008. Navigate like a cabbie: Probabilistic reasoning from observed context-aware behavior. In Proceedings of the 10th international conference on Ubiquitous computing, 322-331. ACM.