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Characterizing preferred motif choices and distance impacts

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  • Jinzhou Cao
  • Qingquan Li
  • Wei Tu
  • Feilong Wang

Abstract

People’s daily travels are structured and can be expressed as networks. Few studies explore how people organize their daily travels and which behavioral principles result in the choices of specific network types. In this study, we first reconstruct location networks and activity networks for numerous individuals from high-resolution mobile phone positioning data and define frequent networks as motifs. The results suggest that 99.9% of people’s travels can be characterized by a limited set of location-based motifs and activity-based motifs. The results further reveal that the least effort principle governs the preferred motif choices through quantifying the rank-frequency properties. The scaling properties of distance characteristically impact motifs, and their scaling differences by node numbers and motif types coincide with the popularities of motifs, verifying the self-adaptions in motif choices; that is, although individuals travel with unique propensities, they always tend to choose the motif with the lowest consumption that satisfies their demand.

Suggested Citation

  • Jinzhou Cao & Qingquan Li & Wei Tu & Feilong Wang, 2019. "Characterizing preferred motif choices and distance impacts," PLOS ONE, Public Library of Science, vol. 14(4), pages 1-17, April.
    • Handle: RePEc:plo:pone00:0215242
    DOI: 10.1371/journal.pone.0215242
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    References listed on IDEAS

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    1. Tang, Jinjun & Zhang, Shen & Zhang, Wenhui & Liu, Fang & Zhang, Weibin & Wang, Yinhai, 2016. "Statistical properties of urban mobility from location-based travel networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 694-707.
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    Cited by:

    1. Shi, Shuyang & Wang, Lin & Wang, Xiaofan, 2022. "Uncovering the spatiotemporal motif patterns in urban mobility networks by non-negative tensor decomposition," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 606(C).
    2. Su, Rongxiang & Xiao, Jingyi & McBride, Elizabeth C. & Goulias, Konstadinos G., 2021. "Understanding senior's daily mobility patterns in California using human mobility motifs," Journal of Transport Geography, Elsevier, vol. 94(C).
    3. Su, Rongxiang & McBride, Elizabeth C. & Goulias, Konstadinos G., 2021. "Unveiling daily activity pattern differences between telecommuters and commuters using human mobility motifs and sequence analysis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 147(C), pages 106-132.
    4. Junwei Ma & Bo Li & Ali Mostafavi, 2024. "Characterizing urban lifestyle signatures using motif properties in network of places," Environment and Planning B, , vol. 51(4), pages 889-903, May.
    5. Ding Ding & Yunhao Zheng & Yi Zhang & Yu Liu, 2024. "Understanding attractions’ connection patterns based on intra-destination tourist mobility: A network motif approach," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-12, December.

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