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Network Centrality of Metro Systems

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Journal PLoS One
Date 2012 Jul 14
PMID 22792373
Citations 16
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Abstract

Whilst being hailed as the remedy to the world's ills, cities will need to adapt in the 21(st) century. In particular, the role of public transport is likely to increase significantly, and new methods and technics to better plan transit systems are in dire need. This paper examines one fundamental aspect of transit: network centrality. By applying the notion of betweenness centrality to 28 worldwide metro systems, the main goal of this paper is to study the emergence of global trends in the evolution of centrality with network size and examine several individual systems in more detail. Betweenness was notably found to consistently become more evenly distributed with size (i.e. no "winner takes all") unlike other complex network properties. Two distinct regimes were also observed that are representative of their structure. Moreover, the share of betweenness was found to decrease in a power law with size (with exponent 1 for the average node), but the share of most central nodes decreases much slower than least central nodes (0.87 vs. 2.48). Finally the betweenness of individual stations in several systems were examined, which can be useful to locate stations where passengers can be redistributed to relieve pressure from overcrowded stations. Overall, this study offers significant insights that can help planners in their task to design the systems of tomorrow, and similar undertakings can easily be imagined to other urban infrastructure systems (e.g., electricity grid, water/wastewater system, etc.) to develop more sustainable cities.

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References
1.
Watts D, Strogatz S . Collective dynamics of 'small-world' networks. Nature. 1998; 393(6684):440-2. DOI: 10.1038/30918. View

2.
Bettencourt L, Lobo J, Strumsky D, West G . Urban scaling and its deviations: revealing the structure of wealth, innovation and crime across cities. PLoS One. 2010; 5(11):e13541. PMC: 2978092. DOI: 10.1371/journal.pone.0013541. View

3.
Bettencourt L, Lobo J, Helbing D, Kuhnert C, West G . Growth, innovation, scaling, and the pace of life in cities. Proc Natl Acad Sci U S A. 2007; 104(17):7301-6. PMC: 1852329. DOI: 10.1073/pnas.0610172104. View

4.
LEAVITT H . Some effects of certain communication patterns on group performance. J Abnorm Psychol. 1951; 46(1):38-50. DOI: 10.1037/h0057189. View

5.
Roth C, Kang S, Batty M, Barthelemy M . A long-time limit for world subway networks. J R Soc Interface. 2012; 9(75):2540-50. PMC: 3427522. DOI: 10.1098/rsif.2012.0259. View