Unravelling daily human mobility motifs
Open accessAbstract
Human mobility is differentiated by time scales. While the mechanism for long time scales has been studied, the underlying mechanism on the daily scale is still unrevealed. Here, we uncover the mechanism responsible for the daily mobility patterns by analysing the temporal and spatial trajectories of thousands of persons as individual networks. Using the concept of motifs from network theory, we find only 17 unique networks are present in daily mobility and they follow simple rules. These networks, called here motifs, are sufficient to capture up to 90 per cent of the population in surveys and mobile phone datasets for different countries. Each individual exhibits a characteristic motif, which seems to be stable over several months. Consequently, daily human mobility can be reproduced by an analytically tractable framework for Markov chains by modelling periods of high-frequency trips followed by periods of lower activity as the key ingredient.
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Analysis of daily travel patterns of individuals using origin-destination matrices indicates stable movement patterns between functionally distinct regions in different cities.
“Analysis of daily travel patterns of individuals using origin-destination matrices indicates stable movement patterns between functionally distinct regions (such as residential and commercial regions) in different cities 177 .”
From From cognitive maps to spatial schemas (2023) · cites this paper as [177]
Mobility patterns of 90 percent of the population in different countries can be captured by repeating 'motifs' of individuals' movements over time.
“Moreover, mobility patterns of 90 percent of the population in different countries can be captured by repeating 'motifs' of individuals' movements over time 177 .”
From From cognitive maps to spatial schemas (2023) · cites this paper as [177]
The pattern of an individual's movements around a city seems to be highly consistent over an observational period of 6 months.
“The pattern of an individual's movements around a city seems to be highly consistent over an observational period of 6 months 177 .”
From From cognitive maps to spatial schemas (2023) · cites this paper as [177]
The patterns of individuals' movement to and from urban hubs across cities can be collected using GIS tools and phone-usage data.
“The patterns of individuals' movement to and from urban hubs across cities can be collected using GIS (geographic information system) tools and phone-usage data 40,43,177,210 .”
From From cognitive maps to spatial schemas (2023) · cites this paper as [177]
Cites
Understanding individual human mobility patterns
González, M. C., Hidalgo, C. A., & Barabási, A.-Ĺ. (2008) · Nature · doi:10.1038/nature06958 · Cited by 6,018 · see citing works
Self-citationNetwork motifs: theory and experimental approaches
Alon, U. (2007) · Nature Reviews Genetics · doi:10.1038/nrg2102 · Cited by 3,279 · see citing works
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 · see citing works
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 · see citing works
The origin of bursts and heavy tails in human dynamics
Barabási, A.-L. (2005) · Nature · doi:10.1038/nature03459 · Cited by 2,210 · see citing works
Chao, Y. R. (1950) · Language · doi:10.2307/409735 · Cited by 2,210 · see citing works
Strategies for mitigating an influenza pandemic
Ferguson, N. M., Cummings, D. A. T., Fraser, C., Cajka, J. C., Cooley, P. C., & Burke, D. S. (2006) · Nature · doi:10.1038/nature04795 · Cited by 2,195 · see citing works
The scaling laws of human travel
Brockmann, D., Hufnagel, L., & Geisel, T. (2006) · Nature · doi:10.1038/nature04292 · Cited by 2,182 · see citing works
Structure and tie strengths in mobile communication networks
Onnela, J.-P. “., Saramäki, J., Hyvönen, J., Szabó, G., Lazer, D., Kaski, K. K., Kertész, J., & Barabási, A.-Ĺ. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0610245104 · Cited by 1,937 · see citing works
Inferring friendship network structure by using mobile phone data
Eagle, N., Pentland, A. (., & Lazer, D. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0900282106 · Cited by 1,856 · see citing works
Multiscale mobility networks and the spatial spreading of infectious diseases
Balcan, D., Vittoria Colizza, A., Gonçalves, B., Hu, H., Ramasco, J. J., & Vespignani, A. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0906910106 · Cited by 1,347 · see citing works
Modelling the scaling properties of human mobility
Song, C., Koren, T., Wang, P., & Barabási, A.-Ĺ. (2010) · Nature Physics · doi:10.1038/nphys1760 · Cited by 1,268 · see citing works
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 · see citing works
Reaction–diffusion processes and metapopulation models in heterogeneous networks
Vittoria Colizza, A., Pastor‐Satorras, R., & Vespignani, A. (2007) · Nature Physics · doi:10.1038/nphys560 · Cited by 877 · see citing works
Synchrony, Waves, and Spatial Hierarchies in the Spread of Influenza
Viboud, C. G., Bjørnstad, O. N., Smith, D. L., Simonsen, L., Miller, M. A., & Grenfell, B. T. (2006) · Science · doi:10.1126/science.1125237 · Cited by 832 · see citing works
Power-Law Scaling in the Brain Surface Electric Potential
Miller, K. J., Sorensen, L. B., Ojemann, J. G., & den Nijs, M. (2009) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000609 · Cited by 742 · see citing works
How Viruses Spread Among Computers and People
Lloyd, A. L., & May, R. M. (2001) · Science · doi:10.1126/science.1061076 · Cited by 661 · see citing works
Uncovering individual and collective human dynamics from mobile phone records
Candia, J., González, M. C., Wang, P., Schoenharl, T., Madey, G., & Barabási, A.-Ĺ. (2008) · Journal of Physics A Mathematical and Theoretical · doi:10.1088/1751-8113/41/22/224015 · Cited by 610 · see citing works
Self-citationObserving the rhythms of daily life: A six-week travel diary
Axhausen, K. W., Zimmermann, A., Schönfelder, S., Rindsfüser, G., & Haupt, T. (2002) · Transportation · doi:10.1023/a:1014247822322 · Cited by 503 · see citing works
Creating synthetic baseline populations
Beckman, R. J., Baggerly, K., & McKay, M. D. (1996) · Transportation Research Part A Policy and Practice · doi:10.1016/0965-8564(96)00004-3 · Cited by 494 · see citing works
Entropy of dialogues creates coherent structures in e-mail traffic
Eckmann, J., Moses, E., & Sergi, D. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0405728101 · Cited by 450 · see citing works
Distribution Theory of Runs: A Markov Chain Approach
Fu, J. C., & Koutras, M. V. (1994) · Journal of the American Statistical Association · doi:10.1080/01621459.1994.10476841 · Cited by 419 · see citing works
Impact of Human Activity Patterns on the Dynamics of Information Diffusion
Iribarren, J. L., & Moro, E. (2009) · Physical Review Letters · doi:10.1103/physrevlett.103.038702 · Cited by 419 · see citing works
A Poissonian explanation for heavy tails in e-mail communication
Malmgren, R. D., Stouffer, D. B., Motter, A. E., & Amaral, L. A. N. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0800332105 · Cited by 398 · see citing works
Habitual travel behaviour: Evidence from a six-week travel diary
Schlich, R., & Axhausen, K. W. (2003) · Transportation · doi:10.1023/a:1021230507071 · Cited by 378 · see citing works
Natural Human Mobility Patterns and Spatial Spread of Infectious Diseases
Belik, V., Geisel, T., & Brockmann, D. (2011) · Physical Review X · doi:10.1103/physrevx.1.011001 · Cited by 337 · see citing works
Self-citationUniversal features of correlated bursty behaviour
Karsai, M., Kaski, K. K., Barabási, A.-Ĺ., & Kertész, J. (2012) · Scientific Reports · doi:10.1038/srep00397 · Cited by 313 · see citing works
Ajelli, M., Gonçalves, B., Balcan, D., Vittoria Colizza, A., Hu, H., Ramasco, J. J., Merler, S., & Vespignani, A. (2010) · BMC Infectious Diseases · doi:10.1186/1471-2334-10-190 · Cited by 301 · see citing works
Temporal motifs in time-dependent networks
Kovanen, L., Karsai, M., Kaski, K. K., Kertész, J., & Saramäki, J. (2011) · Journal of Statistical Mechanics Theory and Experiment · doi:10.1088/1742-5468/2011/11/p11005 · Cited by 300 · see citing works
Generating complete all-day activity plans with genetic algorithms
Charypar, D., & Nagel, K. (2005) · Transportation · doi:10.1007/s11116-004-8287-y · Cited by 277 · see citing works
Characterizing and Modeling the Dynamics of Online Popularity
Ratkiewicz, J., Fortunato, S., Flammini, A., Menczer, F., & Vespignani, A. (2010) · Physical Review Letters · doi:10.1103/physrevlett.105.158701 · Cited by 249 · see citing works
Integration of an Activity-based Model System and a Residential Location Model
Ben-Akiva, M. E., & Bowman, J. L. (1998) · Urban Studies · doi:10.1080/0042098984529 · Cited by 245 · see citing works
Scaling laws of human interaction activity
Diego Rybski, A., Buldyrev, S. V., Havlin, S., Liljeros, F., & Makse, H. A. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0902667106 · Cited by 244 · see citing works
Micro-simulation of daily activity-travel patterns for travel demand forecasting
Kitamura, R., Chen, C., Pendyala, R. M., et al. (2000) · Transportation · doi:10.1023/a:1005259324588 · Cited by 218 · see citing works
Roitberg, B. D. (1993) · Mathematical Biosciences · doi:10.1016/0025-5564(93)90068-l · Cited by 211 · see citing works
Circadian pattern and burstiness in mobile phone communication
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 · see citing works
Atti, M. L. C. D., Merler, S., Rizzo, C., Ajelli, M., Massari, M., Manfredi, P., Furlanello, C., Tomba, G. S., & Iannelli, M. (2008) · PLoS ONE · doi:10.1371/journal.pone.0001790 · Cited by 176 · see citing works
ALBATROSS: Multiagent, Rule-Based Model of Activity Pattern Decisions
Arentze, T., Hofman, F., van Mourik, H., et al. (2000) · Transportation Research Record Journal of the Transportation Research Board · doi:10.3141/1706-16 · Cited by 141 · see citing works
The Nature and Perception of Fluctuations in Human Musical Rhythms
Hennig, H., Fleischmann, R., Fredebohm, A., Hagmayer, Y., Nagler, J., Witt, A., Theis, F. J., & Geisel, T. (2011) · PLoS ONE · doi:10.1371/journal.pone.0026457 · Cited by 100 · see citing works
A Metric of Influential Spreading during Contagion Dynamics through the Air Transportation Network
Nicolaides, C., Cueto‐Felgueroso, L., González, M. C., & Juanes, R. (2012) · PLoS ONE · doi:10.1371/journal.pone.0040961 · Cited by 84 · see citing works
Self-citationCorrelated dynamics in human printing behavior
Harder, U., & Paczuski, M. (2005) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2005.06.079 · Cited by 67 · see citing works
Predicting the Growth and Filtering of At-risk Housing: Structure Ageing, Poverty and Redlining
Margulis, H. L. (1998) · Urban Studies · doi:10.1080/0042098984349 · Cited by 22 · see citing works
Association Rules in Identification of Spatial-Temporal Patterns in Multiday Activity Diary Data
Keuleers, B., Wets, G., Arentze, T., et al. (2001) · Transportation Research Record Journal of the Transportation Research Board · doi:10.3141/1752-05 · Cited by 21 · see citing works
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Returners and explorers dichotomy in human mobility
Pappalardo, L., Simini, F., Rinzivillo, S., Pedreschi, D., Giannotti, F., & Barabási, A.-L. (2015) · Nature Communications · doi:10.1038/ncomms9166 · Cited by 494 · see citing works
Tu, W., Cao, J., Yue, Y., Shaw, S.-L., Zhou, M., Wang, Z., Chang, X., Xu, Y., & Li, Q. (2017) · International Journal of Geographical Information Science · doi:10.1080/13658816.2017.1356464 · Cited by 298 · see citing works
Uncovering the spatial structure of mobility networks
Louail, T., Lenormand, M., Picornell, M., García Cantú, O., Herranz, R., Frias-Martinez, E., Ramasco, J. J., & Barthelemy, M. (2015) · Nature Communications · doi:10.1038/ncomms7007 · Cited by 203 · see citing works
From cognitive maps to spatial schemas
Farzanfar, D., Spiers, H. J., Moscovitch, M., & Rosenbaum, R. S. (2023) · Nature Reviews Neuroscience · doi:10.1038/s41583-022-00655-9 · Cited by 115 · see citing works
Advances in Crowd Analysis for Urban Applications Through Urban Event Detection
Kaiser, M. S., Lwin, K. T., Mahmud, M., Hajializadeh, D., Chaipimonplin, T., Sarhan, A., & Hossain, M. A. (2018) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2017.2771746 · Cited by 98 · see citing works
Spatiotemporal variation in travel regularity through transit user profiling
Manley, E., Zhong, C., & Batty, M. (2018) · Transportation · doi:10.1007/s11116-016-9747-x · Cited by 82 · see citing works
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 · see citing works
- Authors
- Schneider, C. M., Belik, V., Couronné, T., Smoreda, Z., & González, M. C.
- Affiliations
- Massachusetts Institute of Technology (US)
- Max Planck Institute for Dynamics and Self-Organization (DE)
- Orange (France) (FR)
- Corresponding author
- Schneider, Christian M.
- Venue
- Journal of The Royal Society Interface
- Publisher
- The Royal Society
- Published
- 2013-07-06
- Volume
- 10
- Issue
- 84
- Pages
- 20130246
- ISSN
- 1742-5662 (ISSN-L)
- 1742-5689
- PMC
- PMC3673164
- OpenAlex
- W1651166699
- Cited by
- 585
- References
- 43
- Citations by year
- 2026: 27 · 2025: 45 · 2024: 43 · 2023: 26 · 2022: 49 · 2021: 77 · 2020: 55 · 2019: 43 · 2018: 53 · 2017: 51 · 2016: 36 · 2015: 49 · 2014: 23 · 2013: 8
- FWCI
- 90.80
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Journal of The Royal Society Interface (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-sa)
- Max Planck Institute for Plasma Physics (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Topics
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Complex Systems and Time Series Analysis
- Keywords
- human mobility, daily mobility patterns, temporal trajectories, Markov chain model, mobile phone data
- MeSH terms
- Circadian Rhythm, Humans, Markov Chains, Models, Biological, Motor Activity, Travel
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