The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Human mobility modeling at metropolitan scales
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.
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Cites
Differential Privacy: A Survey of Results
Dwork, C. (2008) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-79228-4_1 · Cited by 3,542
Limits of Predictability in Human Mobility
Song, C., Qu, Z., Blumm, N., & Barabási, A.-L. (2010) · Science · doi:10.1126/science.1177170 · Cited by 3,234
Cited by
Human mobility characterization from cellular network data
Becker, R., Cáceres, R., Hanson, K., Isaacman, S., Loh, J. M., Martonosi, M., Rowland, J., Urbanek, S., Varshavsky, A., & Volinsky, C. (2013) · Communications of the ACM · doi:10.1145/2398356.2398375 · Cited by 311
Self-citationShakeri, R., Al-Garadi, M. A., Badawy, A., Mohamed, A., Khattab, T., Al-Ali, A. K., Harras, K. A., & Guizani, M. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2924143 · Cited by 295
Large-Scale Mobile Traffic Analysis: A Survey
Naboulsi, D., Fiore, M., Ribot, S., & Stanica, R. (2016) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2015.2491361 · Cited by 257
Human Mobility Modelling: Exploration and Preferential Return Meet the Gravity Model
Pappalardo, L., Rinzivillo, S., & Simini, F. (2016) · Procedia Computer Science · doi:10.1016/j.procs.2016.04.188 · Cited by 71
coMobile: real-time human mobility modeling at urban scale using multi-view learning
Zhang, D., Zhao, J., Zhang, F., & He, T. (2015) · Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems · doi:10.1145/2820783.2820821 · Cited by 36
Metadata
- Authors
- Isaacman, S., Becker, R., Cáceres, R., Martonosi, M., Rowland, J., Varshavsky, A., & Willinger, W.
- Venue
- Proceedings of the 10th international conference on Mobile systems, applications, and services
- Publisher
- ACM
- Published
- 2012-06-25
- Pages
- 239-252
- OpenAlex
- W2172211955
- Cited by
- 291
- References
- 2
- FWCI
- 37.59
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Isaacman, S., Becker, R., Cáceres, R., Martonosi, M., Rowland, J., Varshavsky, A., & Willinger, W. (2012). Human mobility modeling at metropolitan scales. Proceedings of the 10th international conference on Mobile systems, applications, and services. https://doi.org/10.1145/2307636.2307659
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