The Morphology and Circuity of Walkable and Drivable Street Networks

Boeing, Geoff

doi:10.2139/ssrn.3119939

Cited by 9 publications

(15 citation statements)

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“…Most street networks are nonplanar-due to grade-separated expressways, overpasses, bridges, tunnels, etc.-but most quantitative studies of urban street networks represent them as planar (e.g., Buhl et al, 2006;Cardillo et al, 2006;Barthelemy and Flammini, 2008;Masucci et al, 2009;Strano et al, 2013) for tractability because bridges and tunnels are uncommon in some cities. Planar graphs may reasonably model the street networks of old European town centers, but poorly model the street networks of modern autocentric cities like Los Angeles or Shanghai with many grade-separated expressways, bridges, and underpasses (Boeing, 2018b).…”

Section: Methodsmentioning

confidence: 99%

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A multi-scale analysis of 27,000 urban street networks: Every US city, town, urbanized area, and Zillow neighborhood

Boeing¹

2018

Environment and Planning B: Urban Analytics and City Science

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OpenStreetMap offers a valuable source of worldwide geospatial data useful to urban researchers. This study uses the OSMnx software to automatically download and analyze 27,000 US street networks from OpenStreetMap at metropolitan, municipal, and neighborhood scales-namely, every US city and town, census urbanized area, and Zillow-defined neighborhood. It presents empirical findings on US urban form and street network characteristics, emphasizing measures relevant to graph theory, transportation, urban design, and morphology such as structure, connectedness, density, centrality, and resilience. In the past, street network data acquisition and processing have been challenging and ad hoc. This study illustrates the use of OSMnx and OpenStreetMap to consistently conduct street network analysis with extremely large sample sizes, with clearly defined network definitions and extents for reproducibility, and using nonplanar, directed graphs. These street networks and measures data have been shared in a public repository for other researchers to use.

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Section: Methodsmentioning

confidence: 99%

“…Average circuity measures the ratio of edge lengths to the great-circle distances between the nodes these edges connect, indicating the street pattern's curvilinearity (cf. Boeing, 2018a).…”

Section: Street Network Measuresmentioning

confidence: 99%

A multi-scale analysis of 27,000 urban street networks: Every US city, town, urbanized area, and Zillow neighborhood

Boeing¹

2018

Environment and Planning B: Urban Analytics and City Science

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“…The circuity of the network is an important measure of transportation efficiency and is determined by the transport network configuration, transport planning and the underlying terrain. The circuity in turn shapes how inhabitants use urban space for settlement and travel [4]. Circuity of transit networks has been examined for 36 metropolitan areas (excluding the fringes and low accessible zones) in the United States through maps generated by the OpenStreetMap (OSM) [6].…”

Section: Relationship Between Network Design and Transport Performancementioning

confidence: 99%

“…The term complexity for transport networks results in a rich behaviour arising from system connections, interactions with subsets and the dynamic processes where vehicles and people act within a network structure (pattern and configuration) [3]. In recent years, understanding the structure and dynamics of urban transport networks has been improved through analyses of network topology measures using the mathematical tools of graph theory [4]. The configuration of networks helps to detect travel behaviours of inhabitants [5,6], to evaluate transportation performance [7,8] and to understand how cities are organized [1,9].…”

Section: Introductionmentioning

confidence: 99%

A Macroscopic analysis of transport networks: The influence of network design on urban transportation performance

Dingil¹,

Rupi²,

Stasiškienė³

2019

Int. J. TDI

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This research aims at investigating the direct and indirect influence of network structures on urban transportation performance with a macroscopic perspective. Transport systems are complex -the functional properties of a transportation network can affect mobility patterns which in turn changes the network performance. Understanding the topology of transportation networks is important in order to upgrade transport network design and to improve transportation performance. This paper attempts to determine important network indicators such connectivity, centrality and clustering measures for different network types (road, rail and bike) from 86 urban areas and 32 countries, based on comparable, directly observable open-source data such as OpenStreetMap (OSM) and the TomTom congestion database. Relations between indicators are identified through correlation measures. In addition, regression models are calibrated which quantify the relations between infrastructure accessibility (IA) and network indicators and average traffic delay times. The indicator average road connectivity over average road circuity (RCRC), which is proposed in this study, has not been cited before in literature. The main results suggest that the determination of distance-based connectivity of networks is an important proxy to understand road transportation performance. Consequently, two main results were obtained: (1) an increase in average short-distance connectivity of road networks (average closeness centrality and RCRC) eases road congestion, presumably because the network distributes road traffic more homogenously while decreasing low-permeability choke points, (2) an increase of the average short-distance connectivity of networks of alternative modes such as rail or bike (average weighted rail clustering coefficient and average cycle closeness centrality) does alleviate road congestion. In particular, for cities with over 0.4 km per km 2 cycleway density, an increase in cycleway closeness centrality decreases road congestion and it does so almost as efficiently as an increase in road infrastructure accessibility. Presumably, well-connected, alternative networks with short and direct routes convince car users to shift to the alternative mode, which decreases road traffic volumes.

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“…Recently, scholars have studied street network order and disorder through circuity and orientation entropy. The former measures street curvature and how this relates to other urban patterns and processes (Boeing, 2019;Giacomin & Levinson, 2015;Levinson & El-Geneidy, 2009). The latter quantifies and visualizes the entropy of street orientations to assess how ordered they are (Courtat, Gloaguen, & Douady, 2011;Mohajeri, French, & Gudmundsson, 2013;Mohajeri, French, & Batty, 2013;Mohajeri & Gudmundsson, 2012: entropy measures the fundamentally related concepts of disorder, uncertainty, and dispersion.…”

Section: Street Network Modelingmentioning

confidence: 99%

Urban Spatial Order: Street Network Orientation, Configuration, and Entropy

Boeing

2018

Preprint

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Street networks may be planned according to clear organizing principles or they may evolve organically through accretion, but their configurations and orientations help define a city's spatial logic and order. Measures of entropy can reveal a city's streets' order and disorder. Past studies have explored individual cases of orientation and entropy, but little is known about broader patterns and trends worldwide. This study examines street network orientation, configuration, and entropy in 100 cities around the world using OpenStreetMap data and OSMnx. It measures the entropy of street bearings in weighted and unweighted network models, along with each city's typical street segment length, average circuity, average node degree, and the network's proportions of four-way intersections and dead-ends. It also develops a new indicator of orientation-order that quantifies how a city's street network follows the geometric ordering logic of a single grid. It finds significant statistical relationships between a city's orientation entropy and other indicators of spatial order, including street circuity and measures of connectedness. These indicators, taken in concert, help reveal the extent and nuance of the grid. On average, the US/Canada study sites are far more grid-like than those elsewhere, exhibiting less entropy and circuity. These methods demonstrate automatic, scalable, reproducible tools to empirically measure and visualize city spatial order, illustrating complex urban transportation system patterns and configurations around the world.

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The Morphology and Circuity of Walkable and Drivable Street Networks

Cited by 9 publications

References 50 publications

A multi-scale analysis of 27,000 urban street networks: Every US city, town, urbanized area, and Zillow neighborhood

A multi-scale analysis of 27,000 urban street networks: Every US city, town, urbanized area, and Zillow neighborhood

A Macroscopic analysis of transport networks: The influence of network design on urban transportation performance

Urban Spatial Order: Street Network Orientation, Configuration, and Entropy

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