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.
shows how street networks typically change with location, apparently evolving from grid-like to tree-like. The intermediate stages raise several 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? If so, what indicators can be adopted for further division? From Figure 1
, the existence of representative street network types in different regions of cities may be inferred, thereby suggesting potential laws governing urban network patterns.
Brindle proposed that there are only two major types of street network structures: the grid network and the tree network, distinguished by the degree of connectivity of the roads [2
]. This viewpoint is also the starting point of the present paper. However, this theory does not address the question of how exactly the rest of intermediate networks are different. Due to its strict hierarchical and isolating characteristics, a tree-like network is thought to guarantee pedestrian safety and reduce unwanted through traffic. Although it has been applied in urban planning practice by planning pioneers since the 1930s [3
], there is still no clear theoretical answer.
In the aspect of urban geometry, Strano et al. [5
] divided the European urban road network into different groups by principal component analysis. However, because the research is based on the statistical analysis of the macro urban structure, the results cannot have an impact on small regional land planning. Von and Jaber systematically analyzed the relationship between the structure and texture of urban road network and cultural characteristics in the Middle East, and discussed measures to improve the traditional Arab road network combined with Western ideas [6
Developing a method to describe the structural differences of networks by planning indicators is also a difficult issue. Traditionally, the indicators adopted in street-network planning have usually been related to the geometry of the network: distance, density, area, etc. However, the values of these indicators for the two kinds of networks may sometimes be very close, even when huge differences exist in patterns and functions.
In recent years, research on street network structure has mainly focused on three aspects: hierarchical structure, connection structure, and layout structure [7
]. Hierarchical structure is usually related to the internal composition of the network, which has an important impact on the distribution of traffic flow. Jiang [8
] and Buhl et al. [9
] have provided new evidence as to how a city network is self-organized for available mobility by using geographic information. Marshall described network forms which fundamental impacted travel behavior, distribution of homes and workplaces, land use, and urban form [10
]. Xie and Levinson proposed three new measures, including heterogeneity (entropy), connection pattern, and continuity, specifically examining the structure of urban road networks [11
]. Barthelemy and Flammini have shown in their study of street networks that in the absence of a global design strategy, the development of many different transportation networks does follow a simple, general mechanism [12
The search for a relationship between street network structure and urban land use has become a major research trend [13
]. Researching hierarchical and functional structure, Southworth and Ben-Joseph applied spatial syntax theory to analyze the connection and accessibility of street networks [4
]. This research was partly successful in interpreting how common people understand the spacious structure of a street network by their experiences. Marshall et al. [14
], Southworth et al. [15
], and Lovegrove et al. [16
] used network density, connectivity, and presence of cul-de-sacs, respectively, as essential indicators for analyzing the connection effect and topological relationships of the network. These results show that street network characteristics do play a role in road safety outcomes. Derrible observed the network complexity and robustness of 33 metro systems around the world [17
], and testified that the structure of street networks, therefore, is a result of the interplay between travel cost minimization and efficient land use.
Marshall, Gil et al. used a large amount of data and a variety of analysis methods to study the street network, proposed a network modeling method, and outlined the main street network model features and the complex relationship between different network models [18
]. Boeing′s research on the structure of street networks, which calculates the structural indicators of street networks around the world and classifies them into clusters, helped to reveal the scope and nuances of street networks [19
]. Shi and Wang believed that the street network structure is not simply a hierarchical, connection, or layout structure, but an organic combination of the three [20
]. Porta et al. analyzed the structure and function of urban road network with spatial syntax, which showed that spatial syntax can analyze the in-depth connotation and internal organization of urban road network [21
In addition, the spatial structure analysis of complex networks has also made important breakthroughs. For example, 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
]. These studies have an important reference for later network type identification and network attribute function analysis.
These research results not only show that the road network structure has many variations, but also indicate that the topological parameters of the road network can explain these changes.
4. Sample Analysis
To analyze whether street network types in different regions have any identifiable differences, this study selected 15 urban street networks from around the world, having different shapes and characteristics, as samples.
The number of 15 samples seems to be small, but considering the following reasons, this study considers that these samples show the reliability of the research objectives and results. Although the appearance of the city varies greatly, according to the analysis of Section 2.2
., the main pattern differences of the urban road network lie in the form of intersections and the density of the road network. As a classification study, it is only necessary to find out the characteristics of the reference point or the reference network, and it is not necessary to identify all the networks one by one. In fact, relying too much on large-scale statistical data may not be able to accurately identify road network features, because some features may be submerged in the massive statistical values. In addition, about 30 road network samples were preliminarily selected in this study. In order to express the road network boundary and data distribution clearly, the city samples with similar or repeated patterns and parameters were removed.
By extracting the values of basic attributes from map, such as road-section length, grade, type, and rank of intersections, several indexes of topological structure were evaluated, and then analyzed by ArcGIS software to compare with each other.
4.1. Principles of Sample Selection
Most of the street network samples come from historic Chinese cities, such as Nanjing and Suzhou. Others were taken from a classic scholarly work on streets and patterns [2
The samples represent different parts of urban areas: new development zones, cores of downtown districts, ring areas around the city, and outer suburban residential areas.
For convenience of comparison, each sample’s area was limited to four square kilometers (2 km × 2 km). Considering the influence of ground facilities or terrain features, such as railways and rivers, an irregular boundary was also acceptable when the sample had an obvious shape feature. Figure 5
shows two different examples of urban areas from the sample set.
4.2. Data Collection and Analysis
The data source collection in this paper mainly used OpenStreetMap and ArcGIS to screen, extract, construct, correct, analyze, and abstract street network maps in urban core areas, urban areas, and communities. OpenStreetMap is a global collaborative mapping project that can provide its spatial data through various APIs (Application Programming Interface: are some predefined functions to provide applications and developers with the ability to access a set of routines based on certain software or hardware, but No need to access the source code or understand the details of the internal working mechanism). This database resource is very reliable.The method was simply to collect and screen street network examples, extract street network samples from OpenStreetMap, export OpenStreetMap files, use Geo Converter software to convert them to ESRI Shapefiles, modify the wrong paths in ArcGIS software, and obtain the relevant attributes of each road segment in the sample network. When processing the road network, the edge path of each geometry was buffered by 0.1 km in order to collect the “node types” and “paths” in the road network.
The 15 street network samples may be seen in Figure 6
. The samples can be divided into the four basic pattern types mentioned previously (2.2): standard rectangular grid, T-type, cul-de-sac based, and pure tree-like (see Table 1
The four sample indicators (X-type ratio, T-type ratio, cul-de-sac ratio, and penetrating street ratio) were calculated, and are also marked in Table 1
. It can be seen that the X-type and T-type ratios were well correlated with pattern type. However, the penetrating street ratio was loosely correlated with the X-type and T-type ratios. Therefore, the X-type and T-type ratio were selected as the first factor to identify the tree-like attributes of the road network.
The cul-de-sac ratio, on the other hand, did not correlate well with the pattern type. Figure 7
shows the relationship of T-type ratio and cul-de-sac ratio for all the samples.
From Figure 7
, the T-type ratio is seen to be higher than 0.65 in most sample cities. This indicates that most urban street networks contained a large number of T-intersections. On the other hand, the cul-de-sac ratio of most samples was lower than 0.3.
4.3. Data Analysis and Interpretation
The above results shown that the variations of the indicators (the penetrating street, T-type, X-type, and cul-de-sac ratios) could be used to judge a given street network’s structural subtype.
5. Conclusions and Further Research
This paper tries to answer the most basic question about the geometric structure of urban road networks: how to distinguish between road networks with different geometric forms.
From a topological perspective, both in terms of hierarchical connections and pattern features, the tree-like characteristics concealed in a road network can now be recognized, opening another way to understand the road system besides the traditional grid paradigm.
The main point of this paper is to recognize that all urban road networks, instead of being classified as grids or trees, may be arranged on a spectrum between these extremes. Subdividing the geometric structure of the road network further can be effective for research and engineering technology, improvements in land use, road safety, and transportation efficiency in the future.
Through statistical analysis of road samples in different cities or regions, it has been found that the differences in geometric and topological characteristics between the networks mainly come from the number and the type of intersections, the density and hierarchical structure of the road networks, the proportion of broken roads, and the number and proportion of through-roads. Therefore, a road network can be classified according to how closely it resembles a tree-like network in terms of these various parameters.
This study initially divides road networks into four types (pure tree-like networks, cul-de-sac networks, T-shaped networks, and grid-shaped networks) based on properties such as non-circular traffic, number of end roads, and number of alternative paths.
The research proves that it is possible to use different indicators in a certain order (X-type/T-type ratio, cul-de-sac ratio, and penetrating street ratio) to classify road networks. Four specific, quantitative, well-defined indicators for classifying the small-scale structure of street networks are proposed.
Although the study has found some evidence of pattern differences between networks, some defects existed in the research procedure. The largest problem is that it is difficult to collect attributed data, such as the class of road or the features of the nodes, from the e-map; this may reduce accuracy when analyzing the connectivity of local roads, especially for topological analysis.
Secondly, this study also partly neglected road link attributes, street function, accessibility, and other factors that may further reflect the structure of the street network in some way. On the other hand, adding more indicators would very likely increase the difficulty of the research and the uncertainty of the results.
Additionally, it is found that when different observation scales are used, the indicator value may change, owing to data filtering or boundary effects. This is of great significance for the fractal characteristics of the network, which are another important method of judging the hierarchy and self-similarity of the street system.
On the whole, a tree-like network reflects the basic characteristics of a transportation network better than a grid-like network. Indeed, a grid-like road network can be transformed into a tree-like one by restricting the direction of traffic flow.
Subsequent research on the road network form should not be limited to geometric parameters; the spatial layout of the traffic flow lines should also be considered. Having distinguished the different types of networks, the next step is to analyze the differences in traffic and land use benefits between different micropatterns. This research will promote technological compatibility and coordination between street network and other land use.