sustainability Article Classification of Urban Street Networks Based on Tree-Like Network Features Baorui Han 1 , Dazhi Sun 2,*, Xiaomei Yu 1, Wanlu Song 1 and Lisha Ding 1 1 Department of Traffic Engineering, College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, China; [email protected] (B.H.); [email protected] (X.Y.); [email protected] (W.S.); [email protected] (L.D.) 2 Department of Civil and Architectural Engineering, Texas A&M University-Kingsville, Kingsville, TX 78363, USA * Correspondence: [email protected] Received: 16 December 2019; Accepted: 10 January 2020; Published: 15 January 2020 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 1. Introduction The geometric structure of the street network is determined by the functions the network servers for as well as the physical geographical context. In most cases, the road network has a fixed form because of the nature of the area that it serves; the density and pattern of a network of street blocks are usually determined by location and history. Boeing analyzed 27,000 U.S. street networks, including metropolitan, municipal, and residential areas, and discussed the types of connection (T-intersection ratio, X- intersection ratio, and cul-de-sac ratio) for different types of street networks. This is a remarkable feature of the network form between the city center and the suburbs; that is, the network located in a center usually has a grid-type structure, while those located in suburbs commonly have a branching shape, like that of a tree or a river [1].Other street networks are sometimes classed as belonging to one or the other of these two patterns, but they often have aspects of both; at small scales, there seems to no clear border between grid-like and tree-like patterns distinguishable by conventional traffic planning indicators, such as density and road interval. Sustainability 2020, 12, 628; doi:10.3390/su12020628 www.mdpi.com/journal/sustainability Sustainability 2020, 12, 628 2 of 13 Sustainability 2020, 12, x FOR PEER REVIEW 2 of 13 Figure 11 showsshows howhow streetstreet networksnetworks typicallytypically changechange withwith location,location, apparentlyapparently evolvingevolving fromfrom grid-like to tree-like. The intermediate stages raise several questions.questions. How much does the actual street network at a given stage vary from an ideal grid? Do these irregular-looking networks in fact conform to several basic types?types? If If so so,, what indicators can be adoptedadopted forfor furtherfurther division?division? From Figure 11,, the existence of representative street network types in different different regions of cities may be inferred, thereby suggesting potential lawslaws governing urban network patterns.patterns. FigureFigure 1. 1. FourFour representative representative street street network network types types in in different different regions of a city. 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