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Structural robustness of city road networks based on community
Affiliation:1. School of Science, Hubei University for Nationalities, Enshi 445000, China;2. Department of Civil and Environmental Engineering, School of Engineering, Vanderbilt University, Nashville, TN 37235, USA
Abstract: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.
Keywords:City road networks  Robustness analysis  Community detection  Random attack  Intentional attack
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