Abstract
Anonymous location data from cellular phone networks sheds light on how people move around on a large scale.
- Becker, R., Cáceres, R., Hanson, K., Loh, J.M., Urbanek, S., Varshavsky, A., and Volinsky, C. Route classification using cellular handoff patterns. In Proceedings of the 13th International Conference on Ubiquitous Computing (Beijing, 2011). Google ScholarDigital Library
- Becker, R., Cáceres, R., Hanson, K., Loh, J.M., Urbanek, S., Varshavsky, A., and Volinsky, C. A tale of one city: Using cellular network data for urban planning. IEEE Pervasive Computing 10, 4 (Oct.--Dec. 2011), 18--26. Google ScholarDigital Library
- Bengtsson, L., Lu, X., Thorson, A., Garfield, R., and von Schreeb, J. Improved response to disasters and outbreaks by tracking population movements with mobile phone network data: A post-earthquake geospatial study in Haiti. PLoS Medicine 8, 8 (Aug. 2011).Google ScholarCross Ref
- M.J. Bradley and Associates. Comparison of Energy Use & CO2 Emissions from Different Transportation Modes. Report to American Bus Association, Washington, D.C., May 2007; http://www.buses.org/files/ComparativeEnergy.pdfGoogle Scholar
- Burke, J., Estrin, D., Hansen, M., Parker, A., Ramanathan, N., Reddy, S., and Srivastava, M.B. Participatory sensing. In Proceedings of the Workshop on World-Sensor-Web: Mobile Device-Centric Sensor Networks and Applications (Boulder, CO, Oct. 2006).Google Scholar
- Calabrese, F., Pereira, F., DiLorenzo, G., Liu, L., and Ratti, C. The geography of taste: Analyzing cellphone mobility and social events. In Proceedings of the Eighth International Conference on Pervasive Computing (Helsinki, May 2010). Google ScholarDigital Library
- Cuff, D., Hansen, M., and Kang, J. Urban sensing: Out of the woods. Commun. ACM 51, 3 (Mar. 2008), 24--33. Google ScholarDigital Library
- Girardin, F., Calabrese, F., Dal Fiorre, F., Ratti, C., and Blat, J. Digital footprinting: Uncovering tourists with user-generated content. IEEE Pervasive Computing 7, 4 (Oct--Dec. 2008), 36--43. Google ScholarDigital Library
- Girardin, F., Vaccari, A., Gerber, A., Biderman, A., and Ratti, C. Towards estimating the presence of visitors from the aggregate mobile phone network activity they generate. In Proceedings of the 11th International Conference on Computers in Urban Planning and Urban Management (Hong Kong, June 2009).Google Scholar
- González, M.C., Hidalgo, C.A., and Barabási, A.-L. Understanding individual human mobility patterns. Nature 453, 5 (June 2008), 779--782.Google ScholarCross Ref
- Hidalgo, C.A. and Rodriguez-Sickert, C. The dynamics of a mobile phone network. Physica A: Statistical Mechanics and its Applications 387, 12 (May 2008), 3017--3024.Google ScholarCross Ref
- Isaacman, S., Becker, R., Cáceres, R., Kobourov, S., Martonosi, M., Rowland, J., and Varshavsky, A. Identifying important places in people's lives from cellular network data. In Proceedings of the Ninth International Conference on Pervasive Computing (San Francisco, June 2011). Google ScholarDigital Library
- Isaacman, S., Becker, R., Cáceres, R., Kobourov, S., Martonosi, M., Rowland, J., and Varshavsky, A. Ranges of human mobility in Los Angeles and New York. In Proceedings of the Eighth International Workshop on Managing Ubiquitous Communications and Services (Seattle, Mar. 2011).Google ScholarCross Ref
- Isaacman, S., Becker, R., Cáceres, R., Kobourov, S., Rowland, J., and Varshavsky, A. A tale of two cities. In Proceedings of the 11th ACM Workshop on Mobile Computing Systems and Applications (Annapolis, MD, Feb. 2010). Google ScholarDigital Library
- Isaacman, S., Becker, R., Cáceres, R., Martonosi, M., Rowland, J., Varshavsky, A., and Willinger, W. Human mobility modeling at metropolitan scales. In Proceedings of the 10th ACM Conference on Mobile Systems, Applications, and Services (Lake District, U.K., June 2012). Google ScholarDigital Library
- Mun, M., Reddy, S., Shilton, K., Yau, N., Burke, J., Estrin, D., Hansen, M., Howard, E., West, R., and Boda, P. PEIR, the personal environmental impact report as a platform for participatory sensing systems research. In Proceedings of the Seventh ACM Conference on Mobile Systems, Applications, and Services (Krakow, Poland, June 2009). Google ScholarDigital Library
- N.J. Department of Transportation; http://www.state.nj.us/transportation/Google Scholar
- Pulselli, R., Romano, P., Ratti, C., and Tiezzi, E. Computing urban mobile landscapes through monitoring population density based on cellphone chatting. International Journal of Design and Nature and Ecodynamics 3, 2 (2008).Google ScholarCross Ref
- Ratti, C., Pulselli, R.M., Williams, S., and Frenchman, D. Mobile landscapes: Using location data from cell phones for urban analysis. Environment and Planning B: Planning and Design 33, 5 (2006), 727--748.Google ScholarCross Ref
- Reades, J., Calabrese, F., Sevtsuk, A., and Ratti, C. Cellular census: Explorations in urban data collection. IEEE Pervasive Computing 6, 3 (July--Aug. 2007), 30--38. Google ScholarDigital Library
- Song, C., Qu, Z., Blumm, N., and Barabási, A.-L. Limits of predictability in human mobility. Science 327, 5968 (Feb. 2010), 1018--1021.Google ScholarCross Ref
- Thiagarajan, A., Ravindranath, L.S., Balakrishnan, H., Madden, S., and Girod, L. Accurate, low-energy trajectory mapping for mobile devices. In Proceedings of the Eighth USENIX Symposium on Networked Systems Design and Implementation (Boston, Mar. 2011). Google ScholarDigital Library
- U.S. Bureau of Transportation Statistics. Washington, D.C.; http://www.transtats.bts.govGoogle Scholar
- U.S. Census Bureau. Washington, D.C.; http://www.census.govGoogle Scholar
Index Terms
- Human mobility characterization from cellular network data
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