IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v6y2015i1d10.1038_ncomms9166.html
   My bibliography  Save this article

Returners and explorers dichotomy in human mobility

Author

Listed:
  • Luca Pappalardo

    (Institute of Information Science and Technology (ISTI), National Research Council (CNR)
    University of Pisa
    Center of Network Science, Central European University
    Institute of Physics, Budapest University of Technology and Economics)

  • Filippo Simini

    (Institute of Physics, Budapest University of Technology and Economics
    University of Bristol
    Northeastern University, 110 Forsyth Street, Boston, Massachusetts 02115, USA)

  • Salvatore Rinzivillo

    (Institute of Information Science and Technology (ISTI), National Research Council (CNR))

  • Dino Pedreschi

    (Institute of Information Science and Technology (ISTI), National Research Council (CNR)
    University of Pisa)

  • Fosca Giannotti

    (Institute of Information Science and Technology (ISTI), National Research Council (CNR))

  • Albert-László Barabási

    (Center of Network Science, Central European University
    Northeastern University, 110 Forsyth Street, Boston, Massachusetts 02115, USA
    Brigham and Women's Hospital, Harvard Medical School)

Abstract

The availability of massive digital traces of human whereabouts has offered a series of novel insights on the quantitative patterns characterizing human mobility. In particular, numerous recent studies have lead to an unexpected consensus: the considerable variability in the characteristic travelled distance of individuals coexists with a high degree of predictability of their future locations. Here we shed light on this surprising coexistence by systematically investigating the impact of recurrent mobility on the characteristic distance travelled by individuals. Using both mobile phone and GPS data, we discover the existence of two distinct classes of individuals: returners and explorers. As existing models of human mobility cannot explain the existence of these two classes, we develop more realistic models able to capture the empirical findings. Finally, we show that returners and explorers play a distinct quantifiable role in spreading phenomena and that a correlation exists between their mobility patterns and social interactions.

Suggested Citation

  • Luca Pappalardo & Filippo Simini & Salvatore Rinzivillo & Dino Pedreschi & Fosca Giannotti & Albert-László Barabási, 2015. "Returners and explorers dichotomy in human mobility," Nature Communications, Nature, vol. 6(1), pages 1-8, November.
    • Handle: RePEc:nat:natcom:v:6:y:2015:i:1:d:10.1038_ncomms9166
    DOI: 10.1038/ncomms9166
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/ncomms9166
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/ncomms9166?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Mohammadi, Neda & Taylor, John E., 2017. "Urban energy flux: Spatiotemporal fluctuations of building energy consumption and human mobility-driven prediction," Applied Energy, Elsevier, vol. 195(C), pages 810-818.
    2. Filippo Simini & Gianni Barlacchi & Massimilano Luca & Luca Pappalardo, 2021. "A Deep Gravity model for mobility flows generation," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
    3. Clodomir Santana & Federico Botta & Hugo Barbosa & Filippo Privitera & Ronaldo Menezes & Riccardo Di Clemente, 2023. "COVID-19 is linked to changes in the time–space dimension of human mobility," Nature Human Behaviour, Nature, vol. 7(10), pages 1729-1739, October.
    4. Zhanhong Cheng & Martin Trépanier & Lijun Sun, 2021. "Probabilistic model for destination inference and travel pattern mining from smart card data," Transportation, Springer, vol. 48(4), pages 2035-2053, August.
    5. Wu, Wenbo & Dong, Bing & Wang, Qi (Ryan) & Kong, Meng & Yan, Da & An, Jingjing & Liu, Yapan, 2020. "A novel mobility-based approach to derive urban-scale building occupant profiles and analyze impacts on building energy consumption," Applied Energy, Elsevier, vol. 278(C).
    6. Matteo Böhm & Mirco Nanni & Luca Pappalardo, 2022. "Gross polluters and vehicle emissions reduction," Nature Sustainability, Nature, vol. 5(8), pages 699-707, August.
    7. Natasa Kovacic & Tomislav Car & Ljubica Pilepić Stifanich, 2022. "Transport Behaviour, Perceived Experience And Smart Technology Usage Of Tourist Destination Visitors," Economic Thought and Practice, Department of Economics and Business, University of Dubrovnik, vol. 31(2), pages 439-472, december.
    8. Mohammadi, Neda & Taylor, John E., 2017. "Urban infrastructure-mobility energy flux," Energy, Elsevier, vol. 140(P1), pages 716-728.
    9. Xiangyu Chang & Jingzhou Shen & Xiaoling Lu & Shuai Huang, 2018. "Statistical patterns of human mobility in emerging Bicycle Sharing Systems," PLOS ONE, Public Library of Science, vol. 13(3), pages 1-16, March.
    10. He, Yifan & Zhao, Chen & Zeng, An, 2022. "Ranking locations in a city via the collective home-work relations in human mobility data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 608(P1).
    11. Natalie Coleman & Chenyue Liu & Yiqing Zhao & Ali Mostafavi, 2023. "Lifestyle pattern analysis unveils recovery trajectories of communities impacted by disasters," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-13, December.
    12. Xu, Paiheng & Yin, Likang & Yue, Zhongtao & Zhou, Tao, 2019. "On predictability of time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 345-351.
    13. Yang, Xiong & Zhuge, Chengxiang & Shao, Chunfu & Huang, Yuantan & Hayse Chiwing G. Tang, Justin & Sun, Mingdong & Wang, Pinxi & Wang, Shiqi, 2022. "Characterizing mobility patterns of private electric vehicle users with trajectory data," Applied Energy, Elsevier, vol. 321(C).
    14. Vazifeh, Mohammad M. & Zhang, Hongmou & Santi, Paolo & Ratti, Carlo, 2019. "Optimizing the deployment of electric vehicle charging stations using pervasive mobility data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 121(C), pages 75-91.
    15. Dong, Bing & Liu, Yapan & Fontenot, Hannah & Ouf, Mohamed & Osman, Mohamed & Chong, Adrian & Qin, Shuxu & Salim, Flora & Xue, Hao & Yan, Da & Jin, Yuan & Han, Mengjie & Zhang, Xingxing & Azar, Elie & , 2021. "Occupant behavior modeling methods for resilient building design, operation and policy at urban scale: A review," Applied Energy, Elsevier, vol. 293(C).
    16. Mofeng Yang & Yixuan Pan & Aref Darzi & Sepehr Ghader & Chenfeng Xiong & Lei Zhang, 2022. "A data-driven travel mode share estimation framework based on mobile device location data," Transportation, Springer, vol. 49(5), pages 1339-1383, October.
    17. Huang, Jinyu & Chen, Chao, 2022. "Metapopulation epidemic models with a universal mobility pattern on interconnected networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 591(C).
    18. Chen, Ya & Li, Xue & Zhang, Richong & Huang, Zi-Gang & Lai, Ying-Cheng, 2020. "Instantaneous success and influence promotion in cyberspace — how do they occur?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 556(C).
    19. Yuhui Zhao & Xinyan Zhu & Wei Guo & Bing She & Han Yue & Ming Li, 2019. "Exploring the Weekly Travel Patterns of Private Vehicles Using Automatic Vehicle Identification Data: A Case Study of Wuhan, China," Sustainability, MDPI, vol. 11(21), pages 1-17, November.
    20. Disheng Yi & Yusi Liu & Jiahui Qin & Jing Zhang, 2020. "Identifying Urban Traveling Hotspots Using an Interaction-Based Spatio-Temporal Data Field and Trajectory Data: A Case Study within the Sixth Ring Road of Beijing," Sustainability, MDPI, vol. 12(22), pages 1-20, November.
    21. Xu, Paiheng & Zhang, Rong & Deng, Yong, 2018. "A novel visibility graph transformation of time series into weighted networks," Chaos, Solitons & Fractals, Elsevier, vol. 117(C), pages 201-208.
    22. Katarzyna Sila-Nowicka & A. Stewart Fotheringham & Urška Demšar, 2023. "Activity triangles: a new approach to measure activity spaces," Journal of Geographical Systems, Springer, vol. 25(4), pages 489-517, October.
    23. Li, Heyang & Zeng, An, 2022. "Improving recommendation by connecting user behavior in temporal and topological dimensions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 585(C).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:6:y:2015:i:1:d:10.1038_ncomms9166. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.