Impact analysis of two kinds of failure strategies in Beijing road transportation network

Zundong Zhang, Xiaoyang Xu (), Zhaoran Zhang () and Huijuan Zhou ()
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Zundong Zhang: Beijing Key Lab of Urban Road Transportation, Intelligent Control Technology, North China University of Technology, Beijing 100144, P. R. China
Xiaoyang Xu: Beijing Key Lab of Urban Road Transportation, Intelligent Control Technology, North China University of Technology, Beijing 100144, P. R. China
Zhaoran Zhang: Beijing Key Lab of Urban Road Transportation, Intelligent Control Technology, North China University of Technology, Beijing 100144, P. R. China
Huijuan Zhou: Beijing Key Lab of Urban Road Transportation, Intelligent Control Technology, North China University of Technology, Beijing 100144, P. R. China

International Journal of Modern Physics C (IJMPC), 2018, vol. 29, issue 01, 1-11

Abstract: The Beijing road transportation network (BRTN), as a large-scale technological network, exhibits very complex and complicate features during daily periods. And it has been widely highlighted that how statistical characteristics (i.e. average path length and global network efficiency) change while the network evolves. In this paper, by using different modeling concepts, three kinds of network models of BRTN namely the abstract network model, the static network model with road mileage as weights and the dynamic network model with travel time as weights — are constructed, respectively, according to the topological data and the real detected flow data. The degree distribution of the three kinds of network models are analyzed, which proves that the urban road infrastructure network and the dynamic network behavior like scale-free networks. By analyzing and comparing the important statistical characteristics of three models under random attacks and intentional attacks, it shows that the urban road infrastructure network and the dynamic network of BRTN are both robust and vulnerable.

Keywords: BRTN; random attacks; intentional attacks; average path length; global network efficiency (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1142/S0129183118500055

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