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Journal of Advanced Transportation
Volume 2018, Article ID 8710402, 12 pages
https://doi.org/10.1155/2018/8710402
Research Article

Supervised Land Use Inference from Mobility Patterns

1Transportation Engineering Unit, AICIA, Camino de los Descubrimientos, s/n, 41092 Seville, Spain
2Transportation Engineering, Faculty of Engineering, University of Seville, Camino de los Descubrimientos, s/n, 41092 Seville, Spain

Correspondence should be addressed to Noelia Caceres; se.su.iste@serecacn

Received 29 November 2017; Revised 19 March 2018; Accepted 11 April 2018; Published 21 May 2018

Academic Editor: Giulio E. Cantarella

Copyright © 2018 Noelia Caceres and Francisco G. Benitez. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. S. Çolak, L. P. Alexander, B. G. Alvim, S. R. Mehndiratta, and M. C. González, “Analyzing cell phone location data for urban travel: Current methods, limitations, and opportunities,” Transportation Research Record, vol. 2526, pp. 126–135, 2015. View at Publisher · View at Google Scholar · View at Scopus
  2. M. B. Rojas, E. Sadeghvaziri, and X. Jin, “Comprehensive review of travel behavior and mobility pattern studies that used mobile phone data,” Transportation Research Record, vol. 2563, pp. 71–79, 2016. View at Publisher · View at Google Scholar · View at Scopus
  3. T. Wang, C. Chen, and J. Ma, “Mobile phone data as an alternative data source for travel behavior studies,” in Transportation Research Board 93rd Annual Meeting, Transportation Research Board 93rd Annual Meeting, Washington, D.C, USA, 2014. View at Google Scholar
  4. S. Jiang, G. A. Fiore, Y. Yang, J. Ferreira Jr., E. Frazzoli, and M. C. González, “A review of urban computing for mobile phone traces: Current methods, challenges and opportunities,” in Proceedings of the 2nd ACM SIGKDD International Workshop on Urban Computing, UrbComp 2013 - Held in Conjunction with the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2013, usa, August 2013. View at Publisher · View at Google Scholar · View at Scopus
  5. F. Calabrese, G. Di Lorenzo, L. Liu, and C. Ratti, “Estimating origin-destination flows using mobile phone location data,” IEEE Pervasive Computing, vol. 10, no. 4, pp. 36–44, 2011. View at Publisher · View at Google Scholar · View at Scopus
  6. F. Calabrese, M. Diao, G. Di Lorenzo, J. Ferreira, and C. Ratti, “Understanding individual mobility patterns from urban sensing data: a mobile phone trace example,” Transportation Research Part C: Emerging Technologies, vol. 26, pp. 301–313, 2013. View at Publisher · View at Google Scholar · View at Scopus
  7. P. Widhalm, Y. Yang, M. Ulm, S. Athavale, and M. C. González, “Discovering urban activity patterns in cell phone data,” Transportation, vol. 42, no. 4, pp. 597–623, 2015. View at Publisher · View at Google Scholar · View at Scopus
  8. L. Alexander, S. Jiang, M. Murga, and M. C. González, “Origin-destination trips by purpose and time of day inferred from mobile phone data,” Transportation Research Part C: Emerging Technologies, vol. 58, pp. 240–250, 2015. View at Publisher · View at Google Scholar · View at Scopus
  9. J. L. Toole, S. Colak, B. Sturt, L. P. Alexander, A. Evsukoff, and M. C. González, “The path most traveled: Travel demand estimation using big data resources,” Transportation Research Part C: Emerging Technologies, vol. 58, pp. 162–177, 2015. View at Publisher · View at Google Scholar · View at Scopus
  10. S. Jiang, J. Ferreira, and M. C. Gonzalez, “Clustering daily patterns of human activities in the city,” Data Mining and Knowledge Discovery, vol. 25, no. 3, pp. 478–510, 2012. View at Publisher · View at Google Scholar · View at MathSciNet
  11. Z. Duan, L. Liu, and S. Wang, “MobilePulse: Dynamic profiling of land use pattern and OD matrix estimation from 10 million individual cell phone records in Shanghai,” in Proceedings of the 2011 19th International Conference on Geoinformatics, Geoinformatics 2011, chn, June 2011. View at Publisher · View at Google Scholar · View at Scopus
  12. T. Pei, S. Sobolevsky, C. Ratti, S.-L. Shaw, T. Li, and C. Zhou, “A new insight into land use classification based on aggregated mobile phone data,” International Journal of Geographical Information Science, vol. 28, no. 9, pp. 1988–2007, 2014. View at Publisher · View at Google Scholar · View at Scopus
  13. M. G. Demissie, G. Correia, and C. Bento, “Analysis of the pattern and intensity of urban activities through aggregate cellphone usage,” Transportmetrica A: Transport Science, vol. 11, no. 6, pp. 502–524, 2015. View at Publisher · View at Google Scholar · View at Scopus
  14. F. Calabrese, J. Reades, and C. Ratti, “Eigenplaces: Segmenting space through digital signatures,” IEEE Pervasive Computing, vol. 9, no. 1, pp. 78–84, 2010. View at Publisher · View at Google Scholar · View at Scopus
  15. X. Cheng and W. Li, “Analyzing human activity patterns using cellular phone data: a case study of Jinhe newtown in Shanghai, China,” in Proceedings of the Transportation Research Board 92rd Annual Meeting, Washington, DC, USA, 2013.
  16. J. L. Toole, M. Ulm, M. C. González, and D. Bauer, “Inferring land use from mobile phone activity,” in Proceedings of the the ACM SIGKDD International Workshop, p. 1, Beijing, China, August 2012. View at Publisher · View at Google Scholar
  17. R. Xu and D. Wunsch II, “Survey of clustering algorithms,” IEEE Transactions on Neural Networks and Learning Systems, vol. 16, no. 3, pp. 645–678, 2005. View at Publisher · View at Google Scholar · View at Scopus
  18. G. W. Milligan and M. C. Cooper, “An examination of procedures for determining the number of clusters in a data set,” Psychometrika, vol. 50, no. 2, pp. 159–179, 1985. View at Publisher · View at Google Scholar · View at Scopus
  19. T. Calinski and J. Harabasz, “A dendrite method for cluster analysis,” Communications in Statistics, vol. 3, pp. 1–27, 1974. View at Google Scholar · View at MathSciNet
  20. J. C. Dunn, “A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters,” Journal of Cybernetics, vol. 3, no. 3, pp. 32–57, 1973. View at Publisher · View at Google Scholar · View at MathSciNet
  21. E. Fix and J. L. Hodges, Discriminatory Analysis - Nonparametric Discrimination: Consistency Properties, Hodges. Discriminatory Analysis - Nonparametric Discrimination, USAF School of Aviation Medicine, 1951.
  22. A. Rajaraman and J. D. Ullman, Mining of Massive Datasets, Cambridge University Press, 2011.
  23. S. Anitha Elavarasi and J. Akilandeswari, “Survey on Clustering Algorithm and Similarity Measure for Categorical Data,” ICTACT Journal on Soft Computing, vol. 4, no. 2, pp. 715–722, 2014. View at Publisher · View at Google Scholar
  24. J. Shao, “Linear model selection by cross-validation,” Journal of the American Statistical Association, vol. 88, no. 422, pp. 486–494, 1993. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  25. R. Kohavi and F. Provost, “Special issue on applications of machine learning and the knowledge discovery process,” Mach. Learn, vol. 30, no. 2, pp. 271–274, 1998. View at Publisher · View at Google Scholar