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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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Happiness and the Patterns of Life: A Study of Geolocated Tweets
Frank, M. R., Mitchell, L., Dodds, P. S., & Danforth, C. M. (2013) · Scientific Reports · doi:10.1038/srep02625 · Cited by 156
Incorporating spatial interaction patterns in classifying and understanding urban land use
Liu, X., Kang, C., Gong, L., & Liu, Y. (2015) · International Journal of Geographical Information Systems · doi:10.1080/13658816.2015.1086923 · Cited by 152
Understanding Mobile Traffic Patterns of Large Scale Cellular Towers in Urban Environment
Wang, H., Xu, F., Li, Y., Zhang, P., & Jin, D. (2015) · ACM Internet Measurement Conference (IMC) · doi:10.1145/2815675.2815680 · Cited by 151
Modeling real-time human mobility based on mobile phone and transportation data fusion
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A real-time search engine for the Web of Things
Ostermaier, B., Römer, K., Mattern, F., Fahrmair, M., & Kellerer, W. (2010) · International Conference on the Internet of Things (IoT) · doi:10.1109/iot.2010.5678450 · Cited by 148
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Xia, F., Wang, J., Kong, X., Wang, Z., Li, J., & Chengfei Liu, A. (2018) · IEEE Communications Magazine · doi:10.1109/mcom.2018.1700242 · Cited by 148
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Rocha, L. E. C., & Blondel, V. D. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002974 · Cited by 147
Ding, G., Wang, J., Wu, Q., Yao, Y., Li, R., Zhang, H., & Zou, Y. (2015) · IEEE Communications Magazine · doi:10.1109/mcom.2015.7158283 · Cited by 145
Understanding the bias of call detail records in human mobility research
Zhao, Z., Shaw, S., Xu, Y., Lu, F., Chen, J., & Yin, L. (2016) · International Journal of Geographical Information Systems · doi:10.1080/13658816.2015.1137298 · Cited by 145
Human mobility in response to COVID-19 in France, Italy and UK
Galeazzi, A., Cinelli, M., Bonaccorsi, G., Pierri, F., Schmidt, A. L., Antonio Scala, A., Fabio Pammolli, A., & Quattrociocchi, W. (2021) · Scientific Reports · doi:10.1038/s41598-021-92399-2 · Cited by 145
Cross-Checking Different Sources of Mobility Information
Lenormand, M., Picornell, M., Cantú-Ros, O. G., Tugores, A., Louail, T., Herranz, R., Barthélemy, M., Frı́as, M., & Ramasco, J. J. (2014) · PLoS ONE · doi:10.1371/journal.pone.0105184 · Cited by 153
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Lv, Q., Qiao, Y., Ansari, N., Liu, J., & Yang, J. (2016) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2016.2611654 · Cited by 144
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Phithakkitnukoon, S., Veloso, M., Bento, C. L., Biderman, A., & Ratti, C. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-16917-5_9 · Cited by 143
Tracking Human Mobility Using WiFi Signals
Sapieżyński, P., Stopczynski, A., Gatej, R., & Lehmann, S. (2015) · PLoS ONE · doi:10.1371/journal.pone.0130824 · Cited by 141
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Gao, S. (2014) · Spatial Cognition and Computation · doi:10.1080/13875868.2014.984300 · Cited by 141
Wang, Y., Yuan, N. J., Lian, D., Xu, L., Xie, X., Chen, E., & Rui, Y. (2015) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2783258.2783350 · Cited by 139
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Aledavood, T., López, E., Roberts, S. G. B., Reed‐Tsochas, F., Moro, E., Dunbar, R., & Saramäki, J. (2015) · PLoS ONE · doi:10.1371/journal.pone.0138098 · Cited by 138
Wesolowski, A., Stresman, G. H., Eagle, N., Stevenson, J. C., Owaga, C., Marube, E., Bousema, T., DRAKELEY, C. J., Cox, J., & Buckee, C. O. (2014) · Scientific Reports · doi:10.1038/srep05678 · Cited by 140
Mapping global variation in human mobility
Kraemer, M. U. G., Sadilek, A., Zhang, Q., Marchal, N., Tuli, G., Cohn, E. L., Hswen, Y., Perkins, T. A., Smith, D. L., Reiner, R. C., et al. (2020) · Nature Human Behaviour · doi:10.1038/s41562-020-0875-0 · Cited by 137
Individual mobility prediction using transit smart card data
Zhao, Z., Koutsopoulos, H. N., & Zhao, J. (2018) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2018.01.022 · Cited by 136
The effectiveness of backward contact tracing in networks
Kojaku, S., Hébert‐Dufresne, L., Mones, E., Lehmann, S., & Ahn, Y. (2021) · Nature Physics · doi:10.1038/s41567-021-01187-2 · Cited by 141
Traffic zone division based on big data from mobile phone base stations
Dong, H., Wu, M., Ding, X., Chu, L., Jia, L., Qin, Y., & Zhou, X. (2015) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2015.06.007 · Cited by 136
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Waqas, M., Niu, Y., Li, Y., Ahmed, M., Jin, D., Chen, S., & Han, Z. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2923708 · Cited by 138
Xu, F., Tu, Z., Li, Y., Zhang, P., Fu, X., & Jin, D. (2017) · International World Wide Web Conference (WWW) · doi:10.1145/3038912.3052620 · Cited by 144
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Zhao, K., Tarkoma, S., Liu, S., & Vo, H. T. (2016) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2016.7840811 · Cited by 136
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Pappalardo, L., & Simini, F. (2017) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-017-0548-4 · Cited by 130
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Botta, F., Moat, H. S., & Preis, T. (2015) · Royal Society Open Science · doi:10.1098/rsos.150162 · Cited by 129
Strong and Weak Chaos in Nonlinear Networks with Time-Delayed Couplings
Heiligenthal, S., Dahms, T., Yanchuk, S., Jüngling, T., Flunkert, V., Kanter, I., Schöll, E., & Kinzel, W. (2011) · Physical Review Letters · doi:10.1103/physrevlett.107.234102 · Cited by 129
All your location are belong to us
Li, M., Zhu, H., Gao, Z., Chen, S., Yu, L., Hu, S., & Ren, K. (2014) · ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc) · doi:10.1145/2632951.2632953 · Cited by 129
Data, measurement and empirical methods in the science of science
Liu, L., Jones, B. F., Uzzi, B., & Wang, D. (2023) · Nature Human Behaviour · doi:10.1038/s41562-023-01562-4 · Cited by 143
DP-WHERE: Differentially private modeling of human mobility
Mir, D., Isaacman, S., Cáceres, R., Martonosi, M., & Wright, R. N. (2013) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2013.6691626 · Cited by 135
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Machens, A., Gesualdo, F., Rizzo, C., Tozzi, A. E., Barrat, A., & Cattuto, C. (2013) · BMC Infectious Diseases · doi:10.1186/1471-2334-13-185 · Cited by 125
Kleiminger, W., Mattern, F., & Silvia Santini, B. (2014) · Energy and Buildings · doi:10.1016/j.enbuild.2014.09.046 · Cited by 125
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Liu, Y., Liu, C., Liu, B., Qu, M., & Xiong, H. (2016) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2939672.2939773 · Cited by 125
Mobi-IoST: Mobility-Aware Cloud-Fog-Edge-IoT Collaborative Framework for Time-Critical Applications
Ghosh, S., Mukherjee, A., Ghosh, S. K., & Buyya, R. (2019) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2019.2941754 · Cited by 125
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Chorley, M. J., Whitaker, R. M., & Allen, S. M. (2015) · Computers in Human Behavior · doi:10.1016/j.chb.2014.12.038 · Cited by 124
Fan, Z., Song, X., Shibasaki, R., & Adachi, R. (2015) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2750858.2804277 · Cited by 124
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Cheng, X., Fang, L., Hong, X., & Yang, L. (2017) · IEEE Network · doi:10.1109/mnet.2017.1500295nm · Cited by 123
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Silva, T. H., Viana, A. C., Benevenuto, F., Villas, L. A., Salles, J., Loureiro, A. A. F., & Quercia, D. (2019) · ACM Computing Surveys · doi:10.1145/3301284 · Cited by 123
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Gao, J., Hu, J., Mao, X., & Perc, M. (2012) · Journal of The Royal Society Interface · doi:10.1098/rsif.2011.0846 · Cited by 122
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Future directions in human mobility science
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Gao, J. G., Zhang, Y., & Zhou, T. (2019) · Physics Reports · doi:10.1016/j.physrep.2019.05.002 · Cited by 122
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Understanding mobility in a social petri dish
Szell, M., Sinatra, R., Petri, G., Stefan Thurner, A., & Latora, V. (2012) · Scientific Reports · doi:10.1038/srep00457 · Cited by 116
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Ma, C., Zhu, J., Liu, M., Zhao, H., Liu, N., & Zou, X. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3056396 · Cited by 116
Zhou, J. Z., Zheng, H., Wang, J., Wang, Y., Zhang, B., & Shao, Q. (2019) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2019.2956504 · Cited by 115
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Boyer, D., & Walsh, P. D. (2010) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2010.0275 · Cited by 113
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Huang, P., Lovati, M., Zhang, X., & Bales, C. (2020) · Applied Energy · doi:10.1016/j.apenergy.2020.114983 · Cited by 113
Measures of Human Mobility Using Mobile Phone Records Enhanced with GIS Data
Williams, N. E., Thomas, T. A., Dunbar, M. D., Eagle, N., & Dobra, A. (2015) · PLoS ONE · doi:10.1371/journal.pone.0133630 · Cited by 112
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Lima, A., Stanojević, R., Papagiannaki, D., Rodríguez, P., & González, M. C. (2016) · Journal of The Royal Society Interface · doi:10.1098/rsif.2016.0021 · Cited by 121
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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. (2019) · LaCRIS (University of Lapland) · doi:10.48550/arxiv.1903.12041 · Cited by 112
Li, F., Gui, Z., Zhang, Z., Peng, D., Tian, S. I. P., Yuan, K., Sun, Y., Wu, H., Gong, J., & Lei, Y. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.03.080 · Cited by 114
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Bagrow, J. P., & Lin, Y. (2012) · PLoS ONE · doi:10.1371/journal.pone.0037676 · Cited by 112
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Yang, Y., Herrera, C., Eagle, N., & González, M. C. (2014) · Scientific Reports · doi:10.1038/srep05662 · Cited by 110
Zhang, X., Xu, Y., Tu, W., & Ratti, C. (2018) · Journal of Transport Geography · doi:10.1016/j.jtrangeo.2018.05.002 · Cited by 110
Wang, J., Kong, X., Xia, F., & Sun, L. (2019) · ACM SIGKDD Explorations Newsletter · doi:10.1145/3331651.3331653 · Cited by 109
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Diao, M., Zhu, Y., Ferreira, J., & Ratti, C. (2015) · Environment and Planning B Planning and Design · doi:10.1177/0265813515600896 · Cited by 108
Boonstra, T. W., Nicholas, J., Wong, Q. J. J., Shaw, F., Townsend, S., & Christensen, H. M. (2018) · Journal of Medical Internet Research · doi:10.2196/10131 · Cited by 108
Wang, X., Zhang, Y., Yu, D., Qi, J., & Li, S. (2022) · Land Use Policy · doi:10.1016/j.landusepol.2022.106162 · Cited by 108
Kling, F., & Pozdnoukhov, A. (2012) · ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL GIS) · doi:10.1145/2424321.2424395 · Cited by 108
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Deville, P., Song, C., Eagle, N., Blondel, V. D., Barabási, A.-Ĺ., & Wang, D. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1525443113 · Cited by 111
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Sequences of purchases in credit card data reveal lifestyles in urban populations
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Gao, C., & Liu, J. (2012) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2012.29 · Cited by 105
Zheng, C., Fan, X., Wen, C., Chen, L., Wang, C., & Li, J. (2019) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2019.2932785 · Cited by 107
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Lutu, A., Perino, D., Bagnulo, M., Frı́as, M., & Khangosstar, J. (2020) · ACM Internet Measurement Conference (IMC) · doi:10.1145/3419394.3423655 · Cited by 105
Trajectory Recovery From Ash: User Privacy Is NOT Preserved in Aggregated Mobility DataPreprint
Xu, F., Tu, Z., Li, Y., Zhang, P., Fu, X., & Jin, D. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1702.06270 · Cited by 104
The Places of Our Lives: Visiting Patterns and Automatic Labeling from Longitudinal Smartphone Data
Tri, T. M., & Gática-Pérez, D. (2013) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2013.19 · Cited by 103
Human mobility patterns predict divergent epidemic dynamics among cities
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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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