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Morphology of travel routes and the organization of cities

Author

Listed:
  • Minjin Lee

    (Sungkyunkwan University)

  • Hugo Barbosa

    (University of Rochester)

  • Hyejin Youn

    (Kellogg School of Management at Northwestern University
    Santa Fe Institute
    London Mathematical Lab
    Northwestern University)

  • Petter Holme

    (Institute of Innovative Research, Tokyo Institute of Technology)

  • Gourab Ghoshal

    (University of Rochester
    University of Rochester)

Abstract

The city is a complex system that evolves through its inherent social and economic interactions. Mediating the movements of people and resources, urban street networks offer a spatial footprint of these activities. Of particular interest is the interplay between street structure and its functional usage. Here, we study the shape of 472,040 spatiotemporally optimized travel routes in the 92 most populated cities in the world, finding that their collective morphology exhibits a directional bias influenced by the attractive (or repulsive) forces resulting from congestion, accessibility, and travel demand. To capture this, we develop a simple geometric measure, inness, that maps this force field. In particular, cities with common inness patterns cluster together in groups that are correlated with their putative stage of urban development as measured by a series of socio-economic and infrastructural indicators, suggesting a strong connection between urban development, increasing physical connectivity, and diversity of road hierarchies.

Suggested Citation

  • Minjin Lee & Hugo Barbosa & Hyejin Youn & Petter Holme & Gourab Ghoshal, 2017. "Morphology of travel routes and the organization of cities," Nature Communications, Nature, vol. 8(1), pages 1-10, December.
  • Handle: RePEc:nat:natcom:v:8:y:2017:i:1:d:10.1038_s41467-017-02374-7
    DOI: 10.1038/s41467-017-02374-7
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    Citations

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    Cited by:

    1. 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).
    2. Pengjun Zhao & Hao Wang & Qiyang Liu & Xiao-Yong Yan & Jingzhong Li, 2024. "Unravelling the spatial directionality of urban mobility," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    3. Jinxiao Duan & Guanwen Zeng & Nimrod Serok & Daqing Li & Efrat Blumenfeld Lieberthal & Hai-Jun Huang & Shlomo Havlin, 2023. "Spatiotemporal dynamics of traffic bottlenecks yields an early signal of heavy congestions," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    4. Zexun Chen & Sean Kelty & Alexandre G. Evsukoff & Brooke Foucault Welles & James Bagrow & Ronaldo Menezes & Gourab Ghoshal, 2022. "Contrasting social and non-social sources of predictability in human mobility," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    5. Xia, Nan & Cheng, Liang & Chen, Song & Wei, XiaoYan & Zong, WenWen & Li, ManChun, 2018. "Accessibility based on Gravity-Radiation model and Google Maps API: A case study in Australia," Journal of Transport Geography, Elsevier, vol. 72(C), pages 178-190.
    6. Juste Raimbault, 2018. "Calibration of a density-based model of urban morphogenesis," PLOS ONE, Public Library of Science, vol. 13(9), pages 1-18, September.
    7. Lee, Minjin & Cheon, SangHyun & Son, Seung-Woo & Lee, Mi Jin & Lee, Sungmin, 2023. "Exploring the relationship between the spatial distribution of roads and universal pattern of travel-route efficiency in urban road networks," Chaos, Solitons & Fractals, Elsevier, vol. 174(C).

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