Structural robustness of city road networks based on community

Road network robustness is the ability of a road network to operate correctly under a wide range of attacks. A structural robustness analysis can describe the survivability of a city road network that is under attack and can help improve functions such as urban planning and emergency response. In this paper, a novel approach is presented to quantitatively evaluate road network robustness based on the community structure derived from a city road network, in which communities refer to those densely connected subsets of nodes that are sparsely linked to the remaining network. First, a road network is reconstructed into a set of connected communities. Then, successive simulated attacks are conducted on the reconstructed road networks to test the performance of the networks under attack. The performance of the networks is represented by efficiency and the occurrence of fragmentation. Three attack strategies, including a random attack and two intentional attacks, are performed to evaluate the survivability of the road network under different situations. Contrary to the traditional road segment-based approach, the community-based robustness analysis on a city road network shows distinct structural diversity between communities, providing greater insight into network vulnerability under intentional attacks. Six typical city road networks on three different continents are used to demonstrate the proposed approach. The evaluation results reveal an important feature of the structure of city road networks from a community-based perspective, i.e., that the structure is robust under random failure but fragile under intentional attack. This result is highly consistent in different city road network forms.

[1]  Michael Batty,et al.  A new theory of space syntax , 2004 .

[2]  Mark Newman,et al.  Detecting community structure in networks , 2004 .

[3]  Hani S. Mahmassani,et al.  Hot Spot Management Benefits: Robustness Analysis for a Congested Developing City , 2003 .

[4]  Kirsi Virrantaus,et al.  Identifying Critical Locations in a Spatial Network with Graph Theory , 2008, Trans. GIS.

[5]  M E J Newman,et al.  Finding and evaluating community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[6]  Steven R. Strom GREAT STREETS , 1997, Landscape Journal.

[7]  Lev Muchnik,et al.  Identifying influential spreaders in complex networks , 2010, 1001.5285.

[8]  Bill Hillier,et al.  Space is the machine , 1996 .

[9]  Harry Eugene Stanley,et al.  Catastrophic cascade of failures in interdependent networks , 2009, Nature.

[10]  Jingchun Chen,et al.  Detecting functional modules in the yeast protein-protein interaction network , 2006, Bioinform..

[11]  Réka Albert,et al.  Near linear time algorithm to detect community structures in large-scale networks. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[12]  Beom Jun Kim,et al.  Attack vulnerability of complex networks. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[13]  Harry Eugene Stanley,et al.  Robustness of a Network of Networks , 2010, Physical review letters.

[14]  M. Barthelemy Betweenness centrality in large complex networks , 2003, cond-mat/0309436.

[15]  Marco Pellegrini,et al.  Extraction and classification of dense communities in the web , 2007, WWW '07.

[16]  M A P Taylor,et al.  Network Vulnerability: An Approach to Reliability Analysis at the Level of National Strategic Transport Networks , 2003 .

[17]  D. R. White,et al.  Structural cohesion and embeddedness: A hierarchical concept of social groups , 2003 .

[18]  Yafeng Yin,et al.  Production , Manufacturing and Logistics Robust improvement schemes for road networks under demand uncertainty , 2009 .

[19]  David M Levinson,et al.  Investing for Reliability and Security in Transportation Networks , 2004 .

[20]  Robert C. Thomson,et al.  The’ stroke’ Concept in Geographic Network Generalization and Analysis , 2006 .

[21]  António Pais Antunes,et al.  Interurban road network planning model with accessibility and robustness objectives , 2010 .

[22]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[23]  D. Parkes,et al.  Analysis of Bidding Networks in eBay: Aggregate Preference Identification through Community Detection , 2007 .

[24]  YuanBo,et al.  Detecting functional modules in the yeast protein--protein interaction network , 2006 .

[25]  Darren M. Scott,et al.  Network Robustness Index : a new method for identifying critical links and evaluating the performance of transportation networks , 2006 .

[26]  Claudio Castellano,et al.  Defining and identifying communities in networks. , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[27]  Natali Gulbahce,et al.  The art of community detection , 2008, BioEssays : news and reviews in molecular, cellular and developmental biology.

[28]  Jonas Eliasson,et al.  Regional accessibility analysis from a vulnerability perspective , 2004 .

[29]  Reinhard Diestel,et al.  Graph Theory , 1997 .

[30]  Albert-László Barabási,et al.  Statistical mechanics of complex networks , 2001, ArXiv.

[31]  Bin Jiang,et al.  Self-organized natural roads for predicting traffic flow: a sensitivity study , 2008, 0804.1630.

[32]  Kevin M. Curtin Network Analysis in Geographic Information Science: Review, Assessment, and Projections , 2007 .

[33]  Santo Fortunato,et al.  Community detection in graphs , 2009, ArXiv.

[34]  Vito Latora,et al.  The network analysis of urban streets: A dual approach , 2006 .

[35]  K. Kansky Structure of transportation networks : relationships between network geometry and regional characteristics , 1967 .

[36]  Sharon L. Milgram,et al.  The Small World Problem , 1967 .

[37]  Christophe Claramunt,et al.  Topological Analysis of Urban Street Networks , 2004 .

[38]  David M Levinson,et al.  Measuring the Structure of Road Networks , 2007 .

[39]  Harry Eugene Stanley,et al.  Robustness of interdependent networks under targeted attack , 2010, Physical review. E, Statistical, nonlinear, and soft matter physics.

[40]  Tom Petersen,et al.  Importance and Exposure in Road Network Vulnerability Analysis , 2006 .

[41]  J. Reichardt,et al.  Clustering of sparse data via network communities—a prototype study of a large online market , 2007 .