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Spatial networks
 Physics Reports
"... Complex systems are very often organized under the form of networks where nodes and edges are embedded in space. Transportation and mobility networks, Internet, mobile phone networks, power grids, social and contact networks, neural networks, are all examples where space is relevant and where topolo ..."
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Cited by 14 (1 self)
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Complex systems are very often organized under the form of networks where nodes and edges are embedded in space. Transportation and mobility networks, Internet, mobile phone networks, power grids, social and contact networks, neural networks, are all examples where space is relevant and where topology alone does not contain all the information. Characterizing and understanding
Network Structure and City Size
, 2011
"... Network structure varies across cities. This variation may yield important knowledge about how the internal structure of the city affects its performance. This paper systematically compares a set of surface transportation network structure variables (connectivity, hierarchy, circuity, treeness, entr ..."
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Cited by 6 (4 self)
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Network structure varies across cities. This variation may yield important knowledge about how the internal structure of the city affects its performance. This paper systematically compares a set of surface transportation network structure variables (connectivity, hierarchy, circuity, treeness, entropy, accessibility) across the 50 largest metropolitan areas in the United States. A set of scaling parameters are discovered to show how network size and structure vary with city size. These results suggest that larger cities are physically more interconnected. Hypotheses are presented as to why this might obtain. This paper then consistently measures and ranks access to jobs across 50 US metropolitan areas. It uses that accessibility measure, along with network structure variables and city size to help explain journeytowork time and auto mode share in those cities. A 1 percent increase in accessibility reduces average metropolitan commute times by about 90 seconds each way. A 1 percent increase in network connectivity reduces commute time by 0.1 percent. A 1 percent increase in accessibility results in a 0.0575 percent drop in auto mode share, while a 1 percent increase in treeness reduces auto mode share by 0.061 percent. Use of accessibility and network structure measures is important for planning and evaluating the performance of network investments and land use changes.
Network Structure and Metropolitan Mobility
, 2010
"... The objective of this research to develop quantitative measures that capture various aspects of underlying network structure, using aggregate level travel data from fifty metropolitan areas across the U.S. The influence of these measures on system performance is then tested using statistical regress ..."
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Cited by 3 (2 self)
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The objective of this research to develop quantitative measures that capture various aspects of underlying network structure, using aggregate level travel data from fifty metropolitan areas across the U.S. The influence of these measures on system performance is then tested using statistical regression models. The results corroborate that the quantitative measures of network structure affect the system performance. The results from this analysis can be used to develop network design guidelines that can be used to address current transportation problems.
A routelength efficiency statistic for road networks. Unpublished manuscript. Available at www.stat.berkeley.edu/~aldous/Spatial/ paper.pdf
, 2009
"... This note compares some current theoretical mathematical work on spatial networks with data on intercity road networks within States. In designing a network, a natural constraint is the total length, and a natural objective is to want the route length ℓ(i, j) between typical cities i, j to be not m ..."
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This note compares some current theoretical mathematical work on spatial networks with data on intercity road networks within States. In designing a network, a natural constraint is the total length, and a natural objective is to want the route length ℓ(i, j) between typical cities i, j to be not much longer than straight line distance d(i, j). − 1 for the relative excess routelength. With an ncity network there are () n such numbers r(i, j). A recent theoretical Write r(i, j) = ℓ(i,j) d(i,j) 2 insight is that, from a mathematical viewpoint, a good way to combine these into a single “routelength efficiency ” statistic R ∗ is to define R ∗ as the maximum over d of the average of r(i, j) over citypairs with d(i, j) ≈ d. The optimal tradeoff between total network length and R ∗ is discussed elsewhere in a theoretical model of randomlyplaced cities. What about real networks? For each of 10 U.S. States we studied the road network linking the 20 largest cities. In this note we present the data and discuss the relationship between data and the predictions from theory.
Network Structure and Travel Time Perception
, 2011
"... Research on travel behavior has traditionally focused on ways that infrastructure investments, namely, the urban form and built environment, can be used to influence travel. Proponents argue that overall travel can be reduced by bringing the trip origins and destination closer. Horning et al. (2008) ..."
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Cited by 2 (2 self)
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Research on travel behavior has traditionally focused on ways that infrastructure investments, namely, the urban form and built environment, can be used to influence travel. Proponents argue that overall travel can be reduced by bringing the trip origins and destination closer. Horning et al. (2008) point out that the inherent assumption underlying this argument is the
A Positive Theory of Network Connectivity
, 2010
"... This paper develops a positive theory of network connectivity, seeking to explain the microfoundations of alternative network topologies as the result of selfinterested actors. By building roads, landowners hope to increase their parcels ’ accessibility and economic value. A simulation model is pe ..."
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Cited by 2 (0 self)
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This paper develops a positive theory of network connectivity, seeking to explain the microfoundations of alternative network topologies as the result of selfinterested actors. By building roads, landowners hope to increase their parcels ’ accessibility and economic value. A simulation model is performed on a gridlike land use layer with a downtown in the center, whose structure resembles the early form of many Midwestern and Western (US) cities. The topological attributes for the networks are evaluated. This research posits that road networks experience an evolutionary process where a treelike structure first emerges around the centered parcel before the network pushes outward to the periphery. In addition, road network topology undergoes clear phase changes as the economic values of parcels vary. The results demonstrate that even without a centralized authority, road networks have the property of selforganization and evolution, and, that in the absence of intervention, the treelike or weblike nature of networks is a result of the underlying economics.
Network Structure and Activity Spaces
, 2010
"... This research analyzes the influence of network structure on household spatial patterns, as measured by activity spaces. The analysis uses street network and travel survey data from the Twin Cities and South Florida to compile measures of network structure. Statistical regression models test the rel ..."
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Cited by 1 (1 self)
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This research analyzes the influence of network structure on household spatial patterns, as measured by activity spaces. The analysis uses street network and travel survey data from the Twin Cities and South Florida to compile measures of network structure. Statistical regression models test the relationship between network structure and travel. The results show that network design does influence travel, after controlling for other nonnetwork based measures. Results from this analysis can be used to understand how changes in network can be used to bring about desired changes in travel behavior.
Entropies and Scaling Exponents of Street and Fracture Networks
, 2012
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GRAPH BASED RECOGNITION OF GRID PATTERN IN STREET NETWORKS
"... Pattern recognition is an important step in map generalization. Pattern recognition in street network is significant for street network generalization. A grid is characterized by a set of mostly parallel lines, which are crossed by a second set of parallel lines with roughly right angle. Inspired by ..."
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Pattern recognition is an important step in map generalization. Pattern recognition in street network is significant for street network generalization. A grid is characterized by a set of mostly parallel lines, which are crossed by a second set of parallel lines with roughly right angle. Inspired by object recognition in image processing, this paper presents an approach to the grid recognition in street network based on graph theory. Firstly, the bridges and isolated points of the network are identified and deleted repeatedly. Secondly, the similar orientation graph is created, in which the vertices represent street segments and the edges represent the similar orientation relation between streets. Thirdly, the candidates are extracted through graph operators such as finding connected component, finding maximal complete subgraph, join and intersection. Finally, the candidate are evaluated by deleting bridges and isolated lines repeatedly, reorganizing them into stroke models, changing these stroke models into street intersection graphs in which vertices represent strokes and edges represent strokes intersecting each other, and then calculating the clustering coefficient of these graphs. Experimental result shows the proposed approach is valid in detecting the grid pattern in lower degradation situation.
Network Growth and Ownership Organization
, 2008
"... This dissertation explores transportation development and models the evolutionary growth of transportation networks including its determining factors. In particular, it examines the organization of ownership in the provision of transportation infrastructure as a pivotal driving factor. A series of s ..."
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This dissertation explores transportation development and models the evolutionary growth of transportation networks including its determining factors. In particular, it examines the organization of ownership in the provision of transportation infrastructure as a pivotal driving factor. A series of standalone studies is dedicated to a comprehensive examination of network growth and ownership structure from different approaches analytically, empirically, and in simulation. The analytical model presents a gametheoretic analysis of centralized versus decentralized governance choice on a serial road network. It reveals that, depending on the tradeoff between the benefits and costs associated with alternative decisionmaking processes, governance choice reflects constituents’ collective spending preferences on infrastructure, and may spontaneously shift as the network improves over time. Empirical studies on Minneapolis skyways and Indiana Interurbans examine the expansion of transportation networks as a discrete sequence of link additions over time. Both studies suggest that the deployment of a network has to some extent followed