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Limits of Predictability in Human Mobility
Abstract
Predictable Travel Routines: While people rarely perceive their actions to be random, current models of human activity are fundamentally stochastic. Processes that rely on human mobility patterns, like the prediction of new epidemics, traffic engineering, or city planning, could benefit from highly accurate predictive models. To investigate the predictability of human dynamics, Song et al. (p. 1018 ) used the recorded trajectories of millions of mobile phone users, collected by mobile phone companies and anonymized for research purposes. They hypothesized that given the wide range of travel patterns that different users follow, there would be significant differences between their predictability as well: Users who travel less should be easier to predict than those who are constantly on the road. Surprisingly, there was 93% predictability across the whole user base, and individuals' predictability did not in general fall significantly below 80%.
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“Still, human mobility is known to follow certain universal principles, such as scaling laws of mobility (Alessandretti et al. 2020;Pappalardo et al. 2016;Song et al. 2010b), which suggests that our findings may reflect more general behavioral regularities.”
From Visitation patterns across mobility groups: wandering, commuting, and exploring (2026) · cites this paper as Song et al. 2010b
Citations
Cites
Understanding individual human mobility patterns
González, M. C., Hidalgo, C. A., & Barabási, A.-Ĺ. (2008) · Nature · doi:10.1038/nature06958 · Cited by 6,186
Self-citationSimulating dynamical features of escape panic
Helbing, D., Farkas, I. J., & Vicsek, T. (2000) · Nature · doi:10.1038/35035023 · Cited by 5,005
Statistical physics of social dynamics
Castellano, C., Fortunato, S., & Loreto, V. (2009) · Reviews of Modern Physics · doi:10.1103/revmodphys.81.591 · Cited by 4,119
Reality mining: sensing complex social systems
Eagle, N., & (Sandy) Pentland, A. (2006) · Personal and Ubiquitous Computing · doi:10.1007/s00779-005-0046-3 · Cited by 2,772
The origin of bursts and heavy tails in human dynamics
Barabási, A.-L. (2005) · Nature · doi:10.1038/nature03459 · Cited by 2,210
Self-citationThe scaling laws of human travel
Brockmann, D., Hufnagel, L., & Geisel, T. (2006) · Nature · doi:10.1038/nature04292 · Cited by 2,182
Forecast and control of epidemics in a globalized world
Hufnagel, L., Brockmann, D., & Geisel, T. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0308344101 · Cited by 1,063
Havlin, S., & ben‐Avraham, D. (2002) · Advances In Physics · doi:10.1080/00018730110116353 · Cited by 908
Stochastic Process with Ultraslow Convergence to a Gaussian: The Truncated Lévy Flight
Mantegna, R. N., & Stanley, E. (1994) · Physical Review Letters · doi:10.1103/physrevlett.73.2946 · Cited by 881
Size and form in efficient transportation networks
Banavar, J. R., Maritan, A., & Rinaldo, A. (1999) · Nature · doi:10.1038/20144 · Cited by 856
Flagged first, then by citations
Research works printed but not linked: 3
- Sho W.-S., Kim H. S., IEEE Commun. Mag. 41, 86 (2002).
- N. Navet S.-H. Chen Natural Computing in Computational Finance (Springer Berlin 2008).
- R. M. Fano Transmission of Information (the MIT Press and Wiley New York and London 1961).
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Discovering Spatial Interaction Communities from Mobile Phone D ata
Gao, S., Liu, Y., Wang, Y., & Ma, X. (2013) · Transactions in GIS · doi:10.1111/tgis.12042 · Cited by 247
Is the Voter Model a Model for Voters?
Fernández-Gracia, J., Suchecki, K., Ramasco, J. J., Miguel, M. S., & Eguı́luz, V. M. (2014) · Physical Review Letters · doi:10.1103/physrevlett.112.158701 · Cited by 254
Universal model of individual and population mobility on diverse spatial scales
Yan, X.-Y., Wang, W.-X., Gao, Z.-Y., & Lai, Y. (2017) · Nature Communications · doi:10.1038/s41467-017-01892-8 · Cited by 247
Bui, N., Cesana, M., Hosseini, S. A. H., Liao, Q., Malanchini, I., & Widmer, J. (2017) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2017.2694140 · Cited by 254
The Structure and Dynamics of Cities
Barthélemy, M. (2016) · Cambridge University Press eBooks · doi:10.1017/9781316271377 · Cited by 251
Vazquez‐Prokopec, G. M., Bisanzio, D., Stoddard, S. T., Paz‐Soldán, V. A., Morrison, A. C., Elder, J. P., Ramirez-Paredes, J., Halsey, E. S., Kochel, T. J., Scott, T. W., et al. (2013) · PLoS ONE · doi:10.1371/journal.pone.0058802 · Cited by 252
Big Data for Social Transportation
Zheng, X., Chen, W., Wang, P., Shen, D., Chen, S., Wang, X., Zhang, Q., & Yang, L. (2015) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2015.2480157 · Cited by 243
Rethinking big data: A review on the data quality and usage issues
Liu, J., Li, J., Li, W., & Wu, J. (2015) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2015.11.006 · Cited by 240
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Kang, C., Ma, X., Tong, D., & Liu, Y. (2011) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2011.11.005 · Cited by 239
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Wesolowski, A., Eagle, N., Noor, A. M., Snow, R. W., & Buckee, C. O. (2013) · Journal of The Royal Society Interface · doi:10.1098/rsif.2012.0986 · Cited by 239
Exploring the mobility of mobile phone users
Csáji, B. C., Browet, A., Traag, V., Delvenne, J., Huens, E., Dooren, P. M. V., Smoreda, Z., & Blondel, V. D. (2012) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2012.11.040 · Cited by 240
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He, X., Cormode, G., Machanavajjhala, A., Procopiuc, C. M., & Srivastava, D. (2015) · Proceedings of the VLDB Endowment · doi:10.14778/2809974.2809978 · Cited by 238
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Phase transitions in contagion processes mediated by recurrent mobility patterns
Balcan, D., & Vespignani, A. (2011) · Nature Physics · doi:10.1038/nphys1944 · Cited by 221
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Bernasco, W. (2010) · Criminology · doi:10.1111/j.1745-9125.2010.00190.x · Cited by 219
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Alahi, A., Ramanathan, V., & Fei-Fei, L. (2014) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2014.283 · Cited by 218
Daily Stress Recognition from Mobile Phone Data, Weather Conditions and Individual Traits
Bogomolov, A., Lepri, B., Ferron, M., Pianesi, F., & Pentland, A. (. (2014) · Proceedings of the 22nd ACM international conference on Multimedia · doi:10.1145/2647868.2654933 · Cited by 217
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Schoen, H., Gayo-Avello, D., Metaxas, P., Mustafaraj, E., Strohmaier, M., & Gloor, P. A. (2013) · Internet Research · doi:10.1108/intr-06-2013-0115 · Cited by 217
Mapping Cilento: using geotagged social media data to characterize tourist flows in southern Italy
Chua, A., Servillo, L., Marcheggiani, E., & Moere, A. V. (2016) · Tourism Management · doi:10.1016/j.tourman.2016.06.013 · Cited by 216
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Emergence of bimodality in controlling complex networks
Jia, T., Liu, Y., Csóka, E., Pósfai, M., Slotine, J.-J. E., & Barabási, A.-Ĺ. (2013) · Nature Communications · doi:10.1038/ncomms3002 · Cited by 221
Self-citationGauvin, L., Tizzoni, M., Piaggesi, S., Young, A. J., Adler, N., Verhulst, S., Ferres, L., & Cattuto, C. (2020) · Humanities and Social Sciences Communications · doi:10.1057/s41599-020-0500-x · Cited by 216
Human mobility data in the COVID-19 pandemic: characteristics, applications, and challenges
Hu, T., Wang, S., She, B., Zhang, M., Huang, X., Cui, Y., Khuri, J., Hu, Y., Fu, X., Wang, X., et al. (2021) · International Journal of Digital Earth · doi:10.1080/17538947.2021.1952324 · Cited by 216
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Intra-Urban Human Mobility and Activity Transition: Evidence from Social Media Check-In Data
Wu, L., Zhi, Y., Sui, Z., & Liu, Y. (2014) · PLoS ONE · doi:10.1371/journal.pone.0097010 · Cited by 212
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Peng, C., Jin, X., Wong, K., Shi, M., & Lió, P. (2012) · PLoS ONE · doi:10.1371/journal.pone.0034487 · Cited by 213
Identifying Human Mobility via Trajectory Embeddings
Gao, Q., Zhou, F., Zhang, K., Trajcevski, G., Luo, X., & Zhang, F. (2017) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2017/234 · Cited by 212
Li, J., Li, J., Yuan, Y., & Li, G. (2018) · Cities · doi:10.1016/j.cities.2018.12.008 · Cited by 208
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Li, H., Zhu, H., Du, S., Liang, X., & Shen, X. (2016) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2016.2604383 · Cited by 211
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Xu, J., Wickramarathne, T. L., & Chawla, N. V. (2016) · Science Advances · doi:10.1126/sciadv.1600028 · Cited by 212
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Jo, H.-H., Karsai, M., Kertész, J., & Kaski, K. (2012) · New Journal of Physics · doi:10.1088/1367-2630/14/1/013055 · Cited by 201
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Jiao, J., Venkat, K., Han, Y., & Weissman, T. (2015) · IEEE Transactions on Information Theory · doi:10.1109/tit.2015.2412945 · Cited by 199
Kwan, M. (2016) · Annals of the American Association of Geographers · doi:10.1080/00045608.2015.1117937 · Cited by 206
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Ding, G., Jiao, Y., Wang, J., Zou, Y., Wu, Q., Yao, Y., & Hanzo, L. (2017) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2017.2751058 · Cited by 198
Universal predictability of mobility patterns in cities
Yan, X.-Y., Zhao, C., Fan, Y., Di, Z., & Wang, W.-X. (2014) · Journal of The Royal Society Interface · doi:10.1098/rsif.2014.0834 · Cited by 197
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Yin, M., Sheehan, M., Feygin, S., Paiement, J.-F., & Pozdnoukhov, A. (2018) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2017.2695438 · Cited by 185
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Nadai, M. D., Staiano, J., Larcher, R., Sebe, N., Quercia, D., & Lepri, B. (2016) · International World Wide Web Conference (WWW) · doi:10.1145/2872427.2883084 · Cited by 185
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Coupling human mobility and social ties
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Exploring visitor movement patterns in natural recreational areas
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Combes, R. T. D., Santi, P., Szell, M., Ratti, C., & Oleguer, S. (2016) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 181
Boyer, D., & Salas, C. S. (2014) · Physical Review Letters · doi:10.1103/physrevlett.112.240601 · Cited by 179
Zhong, C., Batty, M., Manley, E., Wang, J., Wang, Z., Chen, F., & Schmitt, G. (2016) · PLoS ONE · doi:10.1371/journal.pone.0149222 · Cited by 175
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Tatem, A. J., Huang, Z., Narib, C., Kumar, U., Kandula, D., Pindolia, D. K., Smith, D. L., Cohen, J. M., Graupe, B., Uusiku, P., et al. (2014) · Malaria Journal · doi:10.1186/1475-2875-13-52 · Cited by 169
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Wang, Q., & Taylor, J. E. (2014) · PLoS ONE · doi:10.1371/journal.pone.0112608 · Cited by 161
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Colley, A., Thebault-Spieker, J., Lin, A. Y., Degraen, D., Fischman, B., Häkkilä, J., Kuehl, K., Nisi, V., Nunes, N., Wenig, N., et al. (2017) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3025453.3025495 · Cited by 159
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Guo, S., Yang, G., Pei, T., Ma, T., Song, C., Shu, H., Du, Y., & Zhou, C. (2018) · Landscape and Urban Planning · doi:10.1016/j.landurbplan.2018.09.016 · Cited by 158
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Information sharing promotes prosocial behaviour
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Social network dynamics of face-to-face interactions
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A brief introduction to distributed systems
van Steen, M. R., & Tanenbaum, A. S. (2016) · Computing · doi:10.1007/s00607-016-0508-7 · Cited by 155
Learning to Simulate Human Mobility
Feng, J., Yang, Z., Xu, F., Yu, H., Wang, M., & Li, Y. (2020) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3394486.3412862 · Cited by 155
Li, Q., Cui, C., Liu, F., Wu, Q., Run, Y., & Han, Z. (2021) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi11010002 · Cited by 155
Zhang, C., Zhang, K., Yuan, Q., Zhang, L., Hanratty, T., & Han, J. (2016) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2939672.2939793 · Cited by 154
Summers, L., & Johnson, S. D. (2016) · Journal of Quantitative Criminology · doi:10.1007/s10940-016-9306-9 · Cited by 154
Exploring human mobility with multi-source data at extremely large metropolitan scales
Zhang, D., Huang, J., Li, Y., Zhang, F., Xu, C., & He, T. (2014) · ACM Annual International Conference on Mobile Computing and Networking (MobiCom) · doi:10.1145/2639108.2639116 · Cited by 152
Metadata
- Authors
- Song, C., Qu, Z., Blumm, N., & Barabási, A.-L.
- Affiliations
- Northeastern University (US)
- Center for Complex Network Research, Departments of Physics, Biology, and Computer Science, Northeastern University, Boston, MA 02115, USA.
- ORCID
- Song, Chaoming
- Venue
- Science
- Published
- 2010-02-19
- Volume
- 327
- Issue
- 5968
- Pages
- 1018-1021
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W1987228002
- Cited by
- 3,234
- References
- 25
- Citations by year
- 2026: 125 · 2025: 113 · 2024: 149 · 2023: 133 · 2022: 206 · 2021: 222 · 2020: 221 · 2019: 274 · 2018: 294 · 2017: 249 · 2016: 258 · 2015: 264 · 2014: 225 · 2013: 203 · 2012: 140
- FWCI
- 390.44
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Complex Systems and Time Series Analysis
- Physical Sciences › Computer Science › Computer Networks and Communications › Opportunistic and Delay-Tolerant Networks
- Keywords
- human mobility, predictability, individual mobility patterns, epidemic forecasting, traffic engineering, urban planning
- MeSH terms
- Behavior, Forecasting, Humans, Human Activities, Locomotion, Probability, Travel, Models, Statistical, Cell Phone
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