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.
Highlights
The geometric structure of the street network is determined by the functions the network servers for as well as the physical geographical context
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
How much does the actual street network at a given stage vary from an ideal grid? Do these irregular-looking networks conformm to several basic types? IIff so, what indicators can be aaddoopted ffoorr ffuurrtthheerr ddiivviissiioonn?? From Figure 1, the existence of reepresentative street network types in difffferent regions of cities may be inferred, thereby suggesting potential laws governing urban netwoorkk ppaatttteerrnnss
Summary
The geometric structure of the street network is determined by the functions the network servers for as well as the physical geographical context. Researching hierarchical and functional structure, Southworth and Ben-Joseph applied spatial syntax theory to analyze the connection and accessibility of street networks [4]. Mocnik’s research into polynomial volume law verified that the spatial dimension of urban road networks is very stable, but the concentration is quite different [22,23] These studies have an important reference for later network type identification and network attribute function analysis. These research results show that the road network structure has many variations, and indicate that the topological parameters of the road network can explain these changes
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