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Classification of Urban Street Networks Based on Tree-Like Network Features

Author

Listed:
  • Baorui Han

    (Department of Traffic Engineering, College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, China)

  • Dazhi Sun

    (Department of Civil and Architectural Engineering, Texas A&M University-Kingsville, Kingsville, TX 78363, USA)

  • Xiaomei Yu

    (Department of Traffic Engineering, College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, China)

  • Wanlu Song

    (Department of Traffic Engineering, College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, China)

  • Lisha Ding

    (Department of Traffic Engineering, College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, China)

Abstract
Urban street networks derive their complexity not only from their hierarchical structure, but also from their tendency to simultaneously exhibit properties of both grid-like and tree-like networks. Using topological indicators based on planning parameters, we develop a method of network division that makes classification of such intermediate networks possible. To quantitatively describe the differences between street network patterns, we first carefully define a tree-like network structure according to topological principles. Based on the requirements of road planning, we broaden this definition and also consider three other types of street networks with different microstructures. We systematically compare the structure variables (connectivity, hierarchy, and accessibility) of selected street networks around the world and find several explanatory parameters (including the relative incidence of through streets, cul-de-sacs, and T-type intersections), which relate network function and features to network type. We find that by measuring a network’s degree of similarity to a tree-like network, we can refine the classification system to more than four classes, as well as easily distinguish between the extreme cases of pure grid-like and tree-like networks. Each indicator has different distinguishing capabilities and is adapted to a different range, thereby permitting networks to be grouped into corresponding types when the indicators are evaluated in a certain order. This research can further improve the theory of interaction between transportation and land use.

Suggested Citation

  • Baorui Han & Dazhi Sun & Xiaomei Yu & Wanlu Song & Lisha Ding, 2020. "Classification of Urban Street Networks Based on Tree-Like Network Features," Sustainability, MDPI, vol. 12(2), pages 1-13, January.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:2:p:628-:d:308879
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    References listed on IDEAS

    as
    1. Geoff Boeing, 2020. "A multi-scale analysis of 27,000 urban street networks: Every US city, town, urbanized area, and Zillow neighborhood," Environment and Planning B, , vol. 47(4), pages 590-608, May.
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    5. Stephen Marshall & Jorge Gil & Karl Kropf & Martin Tomko & Lucas Figueiredo, 2018. "Street Network Studies: from Networks to Models and their Representations," Networks and Spatial Economics, Springer, vol. 18(3), pages 735-749, September.
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    Cited by:

    1. Ming Li & Wei Yu & Jun Zhang, 2023. "Clustering Analysis of Multilayer Complex Network of Nanjing Metro Based on Traffic Line and Passenger Flow Big Data," Sustainability, MDPI, vol. 15(12), pages 1-17, June.
    2. Xin Li & Yongsheng Qian & Junwei Zeng & Xuting Wei & Xiaoping Guang, 2022. "Measurement of Street Network Structure in Strip Cities: A Case Study of Lanzhou, China," Sustainability, MDPI, vol. 14(5), pages 1-17, February.
    3. Sven Eggimann, 2022. "The potential of implementing superblocks for multifunctional street use in cities," Nature Sustainability, Nature, vol. 5(5), pages 406-414, May.

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