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Returners and explorers dichotomy in human mobility

Author

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  • 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
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    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. Yang, Hu & Lv, Sirui & Guo, Bao & Dai, Jianjun & Wang, Pu, 2024. "Uncovering spatiotemporal human mobility patterns in urban agglomerations: A mobility field based approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 637(C).
    13. 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.
    14. 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).
    15. 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.
    16. 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).
    17. 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.
    18. 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).
    19. 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).
    20. 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.
    21. 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.
    22. 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.
    23. 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.
    24. 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).

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