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

Baorui Han, Dazhi Sun, Xiaomei Yu, Wanlu Song and Lisha Ding
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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

Sustainability, 2020, vol. 12, issue 2, 1-13

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.

Keywords: planning parameters; street network; network division; tree-like network (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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