The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
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%.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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).
Cited by
Sorted by citations
Items 501–600 of 1,004
Dynamics of mobile coupled phase oscillators
Uriu, K., Ares, S., Oates, A. C., & Morelli, L. G. (2013) · Physical Review E · doi:10.1103/physreve.87.032911 · Cited by 59
Characterizing International Travel Behavior from Geotagged Photos: A Case Study of Flickr
Yuan, Y., & Medel, M. (2016) · PLoS ONE · doi:10.1371/journal.pone.0154885 · Cited by 59
Population-weighted efficiency in transportation networks
Dong, L., Li, R., Zhang, J., & Di, Z. (2016) · Scientific Reports · doi:10.1038/srep26377 · Cited by 59
Trajectory Prediction Algorithm Based on Gaussian Mixture Model
Gutierrez, L. A. (2015) · Cited by 59
Integrating spatial and temporal contexts into a factorization model for POI recommendation
Cai, L., Xu, J., Liu, J., & Pei, T. (2017) · International Journal of Geographical Information Systems · doi:10.1080/13658816.2017.1400550 · Cited by 59
Trackintel: An open-source Python library for human mobility analysis
Martin, H., Hong, Y., Wiedemann, N., Bucher, D., & Raubal, M. (2023) · Computers, Environment and Urban Systems · doi:10.1016/j.compenvurbsys.2023.101938 · Cited by 58
Predicting human mobility with activity changes
Huang, W., Li, S., Liu, X., & Yifang Ban, A. (2015) · International Journal of Geographical Information Systems · doi:10.1080/13658816.2015.1033421 · Cited by 58
Mohammadi, N., & Taylor, J. E. (2017) · Applied Energy · doi:10.1016/j.apenergy.2017.03.044 · Cited by 58
Ramos, V., Yamaka, W., Alorda, B., & Sriboonchitta, S. (2021) · International Journal of Contemporary Hospitality Management · doi:10.1108/ijchm-10-2020-1170 · Cited by 59
Synthesising digital twin travellers: Individual travel demand from aggregated mobile phone data
Anda, C., Ordóñez, S., & Axhausen, K. W. (2021) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2021.103118 · Cited by 58
Simulating human mobility with a trajectory generation framework based on diffusion model
Chu, C., Zhang, H., Wang, P., & Lu, F. (2024) · International Journal of Geographical Information Systems · doi:10.1080/13658816.2024.2312199 · Cited by 60
Building a validation measure for activity-based transportation models based on mobile phone data
Liu, F., Janssens, D., Cui, J., Wang, Y., Wets, G., & Cools, M. (2014) · Expert Systems with Applications · doi:10.1016/j.eswa.2014.03.054 · Cited by 58
Reliable Topology Design in Time-Evolving Delay-Tolerant Networks with Unreliable Links
Li, F., Chen, S., Huang, M., Yin, Z., Zhang, C., & Wang, Y. (2014) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2014.2345392 · Cited by 58
Shareck, M., Kestens, Y., & Gauvin, L. I. (2013) · International Journal of Health Geographics · doi:10.1186/1476-072x-12-40 · Cited by 58
On the properties of human mobility
Papandrea, M., Jahromi, K. K., Zignani, M., Gaito, S., Giordano, S., & Rossi, G. P. (2016) · Computer Communications · doi:10.1016/j.comcom.2016.03.022 · Cited by 58
On the regularity of human mobility
Oliveira, E. M. R., Viana, A. C., Sarraute, C., Brea, J., & Alvarez-Hamelin, J. I. (2016) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2016.04.005 · Cited by 58
Yang, X., Fang, Z., Xu, Y., Shaw, S., Zhao, Z., Yin, L., Zhang, T., & Lin, Y. (2016) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi5100177 · Cited by 58
Ma, T. (2018) · Remote Sensing · doi:10.3390/rs10071128 · Cited by 58
Route Prediction for Instant Delivery
Zhang, Y., Liu, Y., Li, G., Ding, Y. C., Chen, N., Zhang, H., He, T., & Zhang, D. (2019) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3351282 · Cited by 58
Wu, Y., Wang, L., Fan, L., Yang, M., Zhang, Y., & Feng, Y. (2020) · Cities · doi:10.1016/j.cities.2020.102670 · Cited by 58
Baumann, P. R., Kleiminger, W., & Silvia Santini, B. (2013) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2493432.2493467 · Cited by 57
Autonomous Driving - A Practical Roadmap
Rupp, J. D., & King, A. G. (2010) · SAE technical papers on CD-ROM/SAE technical paper series · doi:10.4271/2010-01-2335 · Cited by 57
Mobility-aware charger deployment for wireless rechargeable sensor networks
Chiu, T.-C., Shih, Y., Pang, A., Jeng, J., & Hsiu, P.-C. (2012) · Asia-Pacific Network Operations and Management Symposium (APNOMS) · doi:10.1109/apnoms.2012.6356102 · Cited by 57
Zhang, Z., He, Q., & Zhu, S. (2017) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2017.10.005 · Cited by 57
Traffic forecasting in cellular networks using the LSTM RNN
Dalgkitsis, A., Louta, M. D., & Karetsos, G. T. (2018) · Panhellenic Conference on Informatics · doi:10.1145/3291533.3291540 · Cited by 57
Li, M.-X., Palchykov, V., Zhi-qiang Jiang, A., Kaski, K., Kertész, J., Miccichè, S., Tumminello, M., Zhou, W.-X., & Mantegna, R. N. (2014) · New Journal of Physics · doi:10.1088/1367-2630/16/8/083038 · Cited by 57
Zhang, X., Zhang, Y., Yu, R., Wang, W., & Guizani, M. (2014) · IEEE Wireless Communications · doi:10.1109/mwc.2014.6812286 · Cited by 56
Characterizing individual mobility perturbations in cities during extreme weather events
Zhang, X., & Li, N. (2022) · International Journal of Disaster Risk Reduction · doi:10.1016/j.ijdrr.2022.102849 · Cited by 56
Human mobility patterns in different communities: a mobile phone data-based social network approach
Shi, L., Chi, G., Liu, X., & Liu, Y. (2015) · Annals of GIS · doi:10.1080/19475683.2014.992372 · Cited by 56
Cellular data meet vehicular traffic theory
Janecek, A., Hummel, K. A., Valerio, D., Ricciato, F., & Hlavacs, H. (2012) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2370216.2370272 · Cited by 56
Mobile Phone Call Data as a Regional Socio-Economic Proxy Indicator
Šćepanović, S., Mishkovski, I., Hui, P., Nurminen, J. K., & Ylä-Jääski, A. (2015) · PLoS ONE · doi:10.1371/journal.pone.0124160 · Cited by 56
Song, X., Shibasaki, R., Yuan, N. J., Xie, X., Li, T., & Adachi, R. (2017) · ACM Transactions on Information Systems · doi:10.1145/3057280 · Cited by 56
Patterns of everyday activities across social contexts
Vagni, G., & Cornwell, B. T. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1718020115 · Cited by 56
Deep Learning-Based Intelligent Dual Connectivity for Mobility Management in Dense Network
Wang, C., Zhao, Z., Sun, Q., & Zhang, H. (2018) · IEEE Vehicular Technology Conference · doi:10.1109/vtcfall.2018.8690554 · Cited by 56
Simple mathematical law benchmarks human confrontations
Johnson, N. F., Medina, P., Zhao, G., Messinger, D. S., Horgan, J., Gill, P., Pinzón, J. C. B., Mattson, W. I., Gangi, D. N., Qi, H., et al. (2013) · Scientific Reports · doi:10.1038/srep03463 · Cited by 55
NextMe: Localization Using Cellular Traces in Internet of Things
Zhang, D., Zhao, S., Yang, L. T., Chen, M., Wang, Y., & Liu, H. (2015) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2015.2389656 · Cited by 55
Classifying call profiles in large-scale mobile traffic datasets
Naboulsi, D., Stanica, R., & Fiore, M. (2014) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom.2014.6848119 · Cited by 55
Modelling of human exposure to air pollution in the urban environment: a GPS-based approach
Dias, D., & Tchepel, O. (2013) · Environmental Science and Pollution Research · doi:10.1007/s11356-013-2277-6 · Cited by 55
A Survey on Machine Learning‐Based Mobile Big Data Analysis: Challenges and Applications
Xie, J., Song, Z., Li, Y., Zhang, Y., Yu, H. T., Zhan, J., Ma, Z., Qiao, Y., Zhang, J., & Guo, J. H. (2018) · Wireless Communications and Mobile Computing · doi:10.1155/2018/8738613 · Cited by 55
Understanding the tourist mobility using GPS: How similar are the tourists?
Zheng, W., Zhou, R., Zhang, Z., Zhong, Y., Wang, S., Wei, Z., & Ji, H. (2018) · Tourism Management · doi:10.1016/j.tourman.2018.09.019 · Cited by 55
“May the Force move you”: Roles and actors of information sharing devices in urban mobility
Vecchio, G., & Tricarico, L. (2018) · Cities · doi:10.1016/j.cities.2018.11.007 · Cited by 55
Lu, M., An, K., Hsu, S., & Zhu, R. (2019) · Sustainable Cities and Society · doi:10.1016/j.scs.2019.101567 · Cited by 55
A Prediction-Based Resource Matching Scheme for Rentable LEO Satellite Communication Network
Han, C., Liu, A., Huo, L., Wang, H., & Liang, X. (2019) · IEEE Communications Letters · doi:10.1109/lcomm.2019.2955469 · Cited by 54
Scale, context, and heterogeneity: the complexity of the social space
Balsa‐Barreiro, J., Menéndez, M., & Morales, A. J. (2022) · Scientific Reports · doi:10.1038/s41598-022-12871-5 · Cited by 54
A Model for Throughput Prediction for Mobile Users
Bui, N., Michelinakis, F., & Widmer, J. (2014) · IMDEA Networks Institute Digital Repository · Cited by 54
TOWARDS A STATISTICAL PHYSICS OF HUMAN MOBILITY
Gallotti, R., Bazzani, A., & Rambaldi, S. (2012) · International Journal of Modern Physics C · doi:10.1142/s0129183112500611 · Cited by 54
Lu, S., Fang, Z., Zhang, X., Shaw, S., Yin, L., Zhao, Z., & Yang, X. (2017) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi6010007 · Cited by 54
Strengthening Insights in Microbial Ecological Networks from Theory to Applications
Lv, X., Zhao, K., Xue, R., Liu, Y., Xu, J. M., & Ma, B. (2019) · mSystems · doi:10.1128/msystems.00124-19 · Cited by 54
Mobile Network Data for Public Health: Opportunities and Challenges
Oliver, N., Matic, A., & Frı́as-Martı́nez, E. (2015) · Frontiers in Public Health · doi:10.3389/fpubh.2015.00189 · Cited by 53
User mobility from the view of cellular data networks
Zhang, Y. (2014) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom.2014.6848068 · Cited by 53
Ilias Leontiadis, A., Lima, A., Kwak, H., Stanojević, R., Wetherall, D. J., & Papagiannaki, K. (2014) · ACM International Conference on emerging Networking EXperiments and Technologies (CoNEXT) · doi:10.1145/2674005.2674982 · Cited by 53
PIS: A Multi-Dimensional Routing Protocol for Socially-Aware Networking
Xia, F., Liu, L., Jedari, B., & Das, S. K. (2016) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2016.2517649 · Cited by 53
Data-Driven Evaluation of Anticipatory Networking in LTE Networks
Bui, N., & Widmer, J. (2018) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2018.2809750 · Cited by 53
Econometrics and Machine Learning
Arthur Charpentier, A., Flachaire, E., & Ly, A. (2019) · Economie et Statistique / Economics and Statistics · doi:10.24187/ecostat.2018.505d.1970 · Cited by 928
Unveiling large-scale commuting patterns based on mobile phone cellular network data
Hadachi, A., Pourmoradnasseri, M., & Khoshkhah, K. (2020) · Journal of Transport Geography · doi:10.1016/j.jtrangeo.2020.102871 · Cited by 53
Measuring migration 2.0: a review of digital data sources
Tjaden, J. (2021) · Comparative Migration Studies · doi:10.1186/s40878-021-00273-x · Cited by 52
Effect of time-activity adjustment on exposure assessment for traffic-related ultrafine particles
Lane, K. J., Levy, J. I., Scammell, M. K., Patton, A. P., Durant, J. L., Mwamburi, M., Zamore, W., & Brugge, D. (2015) · Journal of Exposure Science & Environmental Epidemiology · doi:10.1038/jes.2015.11 · Cited by 52
On the validity of geosocial mobility traces
Zhang, Z., Zhou, L., Zhao, X., Wang, G., Su, Y., Metzger, M., Zheng, H., & Zhao, B. Y. (2013) · Proceedings of the Twelfth ACM Workshop on Hot Topics in Networks · doi:10.1145/2535771.2535786 · Cited by 52
Li, Y., Song, C., Jin, D., & Chen, S. (2014) · IEEE Wireless Communications · doi:10.1109/mwc.2014.6940433 · Cited by 52
Self-citationMeasuring social networks in British primary schools through scientific engagement
Conlan, A. J. K., Eames, K. T. D., Gage, J. A., von Kirchbach, J. C., Ross, J. V., Saenz, R. A., & Gog, J. R. (2010) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2010.1807 · Cited by 56
Reconstructing Individual Mobility from Smart Card Transactions: A Space Alignment Approach
Yuan, N. J., Wang, Y., Zhang, F., Xie, X., & Sun, G. (2013) · Proceedings · doi:10.1109/icdm.2013.37 · Cited by 52
Using higher-order Markov models to reveal flow-based communities in networks
Salnikov, V., Schaub, M. T., & Lambiotte, R. (2016) · Scientific Reports · doi:10.1038/srep23194 · Cited by 57
Data-driven model for passenger route choice in urban metro network
Wu, J., Qu, Y., Sun, H., Yin, H., Yan, X.-Y., & Zhao, J. (2019) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2019.04.231 · Cited by 52
Li, M., Gao, S., Lu, F., Liu, K., Zhang, H., & Tu, W. (2021) · International Journal of Geographical Information Systems · doi:10.1080/13658816.2021.1912347 · Cited by 55
Gu, H., Shen, J., & Chu, J. (2022) · Annals of the American Association of Geographers · doi:10.1080/24694452.2022.2097050 · Cited by 51
Rao, J., Gao, S., & Zhu, S. (2023) · International Journal of Geographical Information Systems · doi:10.1080/13658816.2023.2262550 · Cited by 51
Picornell, M., Ruíz, T., Lenormand, M., Ramasco, J. J., Dubernet, T. J. P., & Frı́as, M. (2015) · Transportation · doi:10.1007/s11116-015-9594-1 · Cited by 51
Predictive green wireless access: exploiting mobility and application information
Abou-Zeid, H., & Hassanein, H. S. (2013) · IEEE Wireless Communications · doi:10.1109/mwc.2013.6664479 · Cited by 51
Intelligent System for Human Behavior Analysis and Reasoning Following Large-Scale Disasters
Song, X., Zhang, Q., Sekimoto, Y., Horanont, T., Ueyama, S., & Shibasaki, R. (2013) · IEEE Intelligent Systems · doi:10.1109/mis.2013.35 · Cited by 51
Effect of memory on the dynamics of random walks on networks
Lambiotte, R., Salnikov, V., & Rosvall, M. (2014) · Journal of Complex Networks · doi:10.1093/comnet/cnu017 · Cited by 51
Mobility Viewer: An Eulerian Approach for Studying Urban Crowd Flow
Ma, Y., Lin, T., Cao, Z., Li, C., Wang, F., & Chen, W. (2015) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2015.2498187 · Cited by 51
Learning to Predict Human Behavior in Crowded Scenes
Alahi, A., Ramanathan, V., Goel, K., Robicquet, A., Sadeghian, A., Fei-Fei, L., & Savarese, S. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-809276-7.00011-4 · Cited by 57
How friends share urban space: An exploratory spatiotemporal analysis using mobile phone data
Xu, Y., Belyi, A., Bojić, I., & Ratti, C. (2017) · Transactions in GIS · doi:10.1111/tgis.12285 · Cited by 51
Mottaghinia, Z., & Ghaffari, A. (2018) · Wireless Personal Communications · doi:10.1007/s11277-018-5360-y · Cited by 51
Online Deep Ensemble Learning for Predicting Citywide Human Mobility
Fan, Z., Song, X., Xia, T., Jiang, R., Shibasaki, R., & Sakuramachi, R. (2018) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3264915 · Cited by 51
SOON: self-optimizing optical networks with machine learning
Zhao, Y., Yan, B., Liu, D., He, Y., Wang, D., & Zhang, J. (2018) · Optics Express · doi:10.1364/oe.26.028713 · Cited by 51
Understanding Human Mobility Patterns in a Developing Country Using Mobile Phone Data
Demissie, M. G., Phithakkitnukoon, S., Kattan, L., & Farhan, A. (2019) · Data Science Journal · doi:10.5334/dsj-2019-001 · Cited by 51
Crowd intelligence for sustainable futuristic intelligent transportation system: a review
Shit, R. C. (2020) · IET Intelligent Transport Systems · doi:10.1049/iet-its.2019.0321 · Cited by 51
Impacts of COVID-19 on bike-sharing usages in Seoul, South Korea
Jiao, J., Lee, H., & Choi, S. J. (2022) · Cities · doi:10.1016/j.cities.2022.103849 · Cited by 50
Spatial-Temporal Interval Aware Sequential POI Recommendation
Wang, E., Jiang, Y., Xu, Y., Wang, L., & Yang, Y. (2022) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde53745.2022.00202 · Cited by 50
SPRINT: Social prediction-based opportunistic routing
Ciobanu, R., Dobre, C., & Cristea, V. (2013) · IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM) · doi:10.1109/wowmom.2013.6583442 · Cited by 50
Stopczynski, A., Larsen, J. E., Lehmann, S., Dynovski, L. D., & Fuentes, M. (2013) · IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) · doi:10.1109/percomw.2013.6529489 · Cited by 50
Touristic site attractiveness seen through Twitter
Bassolas, A., Lenormand, M., Tugores, A., Gonçalves, B., & Ramasco, J. J. (2016) · EPJ Data Science · doi:10.1140/epjds/s13688-016-0073-5 · Cited by 53
Trajectory Prediction for Ocean Vessels Base on K-order Multivariate Markov Chain
Guo, S., Liu, C., Guo, Z., Feng, Y., Hong, F., & Huang, H. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-94268-1_12 · Cited by 50
Ultra-Dense HetNets Meet Big Data: Green Frameworks, Techniques, and Approaches
Li, Y., Zhang, Y., Luo, K. B., Jiang, T., Li, Z., & Peng, W. (2018) · IEEE Communications Magazine · doi:10.1109/mcom.2018.1700425 · Cited by 50
Spatiotemporal Mobility Prediction in Proactive Self-Organizing Cellular Networks
Farooq, H., & Imran, M. A. (2016) · IEEE Communications Letters · doi:10.1109/lcomm.2016.2623276 · Cited by 49
Motivating Human-Enabled Mobile Participation for Data Offloading
Zhang, X., Guo, L., Li, M., & Fang, Y. M. (2017) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2017.2773087 · Cited by 49
Examining public transport usage by older adults with smart card data: A longitudinal study in Japan
Liu, S., Yamamoto, T., Yao, E., & Nakamura, T. (2021) · Journal of Transport Geography · doi:10.1016/j.jtrangeo.2021.103046 · Cited by 49
Wang, Z., Xia, T., Jiang, R., Liu, X., Kim, K., Song, X., & Shibasaki, R. (2021) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde51399.2021.00154 · Cited by 49
Influential Factors of Intercity Patient Mobility and its Network Structure in China
Ding, J., Yang, C., Wang, Y., Li, P., Wang, F., Kang, Y., Wang, H., Liang, Z., Zhang, J., Han, P., et al. (2022) · Cities · doi:10.1016/j.cities.2022.103975 · Cited by 50
Digital daily cycles of individuals
Aledavood, T., Lehmann, S., & Saramäki, J. (2015) · Frontiers in Physics · doi:10.3389/fphy.2015.00073 · Cited by 49
Mobile location tracking in metro areas
Husted, N., & Myers, S. (2010) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/1866307.1866318 · Cited by 49
Uncovering Measurements of Social and Demographic Behavior From Smartphone Location Data
Kelly, D., Smyth, B., & Caulfield, B. M. (2013) · IEEE Transactions on Human-Machine Systems · doi:10.1109/tsmc.2013.2238926 · Cited by 49
Safety-Information-Driven Human Mobility Patterns with Metapopulation Epidemic Dynamics
Wang, B., Cao, L., Suzuki, H., & Aihara, K. (2012) · Scientific Reports · doi:10.1038/srep00887 · Cited by 50
Collaborative calibration and sensor placement for mobile sensor networks
Xiang, Y., Bai, L. S., Piedrahita, R., Dick, R. P., Lv, Q., Hannigan, M., & Shang, L. (2012) · ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) · doi:10.1109/ipsn.2012.6920952 · Cited by 49
Spatiotemporal Data from Mobile Phones for Personal Mobility Assessment
Smoreda, Z., Olteanu‐Raimond, A., & Couronné, T. (2013) · Emerald Group Publishing Limited eBooks · doi:10.1108/9781781902882-041 · Cited by 49
Spatiotemporal model for assessing the stability of urban human convergence and divergence patterns
Fang, Z., Yang, X., Xu, Y., Shaw, S., & Yin, L. (2017) · International Journal of Geographical Information Systems · doi:10.1080/13658816.2017.1346256 · Cited by 49
Boosting Vehicle-to-Cloud Communication by Machine Learning-Enabled Context Prediction
Sliwa, B., Falkenberg, R., Liebig, T., Piatkowski, N., & Wietfeld, C. (2019) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2019.2930109 · Cited by 49
Malik, S., & Kim, D. (2019) · Sustainability · doi:10.3390/su11123357 · Cited by 49
Zhou, X., & Yeh, A. G. O. (2020) · Transportation · doi:10.1007/s11116-020-10094-z · Cited by 49
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review