A Combined DTA Approach for Road Network Robustness Analysis

Quantifying Road-Network Robustness toward Flood-Resilient Transportation Systems

Sustainability ◽

10.3390/su13063172 ◽

2021 ◽

Vol 13 (6) ◽

pp. 3172

Author(s):

Suchat Tachaudomdach ◽

Auttawit Upayokin ◽

Nopadon Kronprasert ◽

Kriangkrai Arunotayanun

Keyword(s):

Road Network ◽

Critical Infrastructure ◽

Policy Issue ◽

Analytical Framework ◽

Transportation Systems ◽

Network Robustness ◽

System Response ◽

Chiang Mai ◽

Protection Measures ◽

Response And Recovery

Amidst sudden and unprecedented increases in the severity and frequency of climate-change-induced natural disasters, building critical infrastructure resilience has become a prominent policy issue globally for reducing disaster risks. Sustainable measures and procedures to strengthen preparedness, response, and recovery of infrastructures are urgently needed, but the standard for measuring such resilient elements has yet to be consensually developed. This study was undertaken with an aim to quantitatively measure transportation infrastructure robustness, a proactive dimension of resilience capacities and capabilities to withstand disasters; in this case, floods. A four-stage analytical framework was empirically implemented: 1) specifying the system and disturbance (i.e., road network and flood risks in Chiang Mai, Thailand), 2) illustrating the system response using the damaged area as a function of floodwater levels and protection measures, 3) determining recovery thresholds based on land use and system functionality, and 4) quantifying robustness through the application of edge- and node-betweenness centrality models. Various quantifiable indicators of transportation robustness can be revealed; not only flood-damaged areas commonly considered in flood-risk management and spatial planning, but also the numbers of affected traffic links, nodes, and cars are highly valuable for transportation planning in achieving sustainable flood-resilient transportation systems.

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Interdependency and Vulnerability of Multipartite Networks under Target Node Attacks

Complexity ◽

10.1155/2019/2680972 ◽

2019 ◽

Vol 2019 ◽

pp. 1-16

Author(s):

Qing Cai ◽

Mahardhika Pratama ◽

Sameer Alam

Keyword(s):

Complex Networks ◽

Robustness Analysis ◽

Network Models ◽

Continuous Phase ◽

Structure Design ◽

Network Robustness ◽

Multilayer Networks ◽

Component Theory ◽

Centrality Metrics ◽

Entire Network

Complex networks in reality may suffer from target attacks which can trigger the breakdown of the entire network. It is therefore pivotal to evaluate the extent to which a network could withstand perturbations. The research on network robustness has proven as a potent instrument towards that purpose. The last two decades have witnessed the enthusiasm on the studies of network robustness. However, existing studies on network robustness mainly focus on multilayer networks while little attention is paid to multipartite networks which are an indispensable part of complex networks. In this study, we investigate the robustness of multipartite networks under intentional node attacks. We develop two network models based on the largest connected component theory to depict the cascading failures on multipartite networks under target attacks. We then investigate the robustness of computer-generated multipartite networks with respect to eight node centrality metrics. We discover that the robustness of multipartite networks could display either discontinuous or continuous phase transitions. Interestingly, we discover that larger number of partite sets of a multipartite network could increase its robustness which is opposite to the phenomenon observed on multilayer networks. Our findings shed new lights on the robust structure design of complex systems. We finally present useful discussions on the applications of existing percolation theories that are well studied for network robustness analysis to multipartite networks. We show that existing percolation theories are not amenable to multipartite networks. Percolation on multipartite networks still deserves in-depth efforts.

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Road Network Robustness for Avoiding Functional Isolation in Disasters

Journal of Transportation Engineering ◽

10.1061/(asce)0733-947x(2004)130:5(560) ◽

2004 ◽

Vol 130 (5) ◽

pp. 560-567 ◽

Cited By ~ 66

Author(s):

Hiroyuki Sakakibara ◽

Yoshio Kajitani ◽

Norio Okada

Keyword(s):

Road Network ◽

Network Robustness

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A multiple perspective method for urban subway network robustness analysis

AIP Advances ◽

10.1063/1.5023766 ◽

2018 ◽

Vol 8 (7) ◽

pp. 075219 ◽

Cited By ~ 2

Author(s):

Shuliang Wang ◽

Sen Nie ◽

Longfeng Zhao ◽

H. Eugene Stanley

Keyword(s):

Robustness Analysis ◽

Network Robustness ◽

Multiple Perspective

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Stock Market Temporal Complex Networks Construction, Robustness Analysis, and Systematic Risk Identification: A Case of CSI 300 Index

Complexity ◽

10.1155/2020/7195494 ◽

2020 ◽

Vol 2020 ◽

pp. 1-19

Author(s):

Xiaole Wan ◽

Zhen Zhang ◽

Chi Zhang ◽

Qingchun Meng

Keyword(s):

Stock Market ◽

Complex Networks ◽

Robustness Analysis ◽

Risk Identification ◽

Network Robustness ◽

Degree Centrality ◽

Market Risks ◽

Stationary Test ◽

Systematic Risks ◽

Quantitative Indexes

The Chinese stock 300 index (CSI 300) is widely accepted as an overall reflection of the general movements and trends of the Chinese A-share markets. Among the methodologies used in stock market research, the complex network as the extension of graph theory presents an edged tool for analyzing internal structure and dynamic involutions. So, the stock data of the CSI 300 were chosen and divided into two time series, prepared for analysis via network theory. After stationary test and coefficients calculated for daily amplitudes of stock, two “year-round” complex networks were constructed, respectively. Furthermore, the network indexes, including out degree centrality, in degree centrality, and betweenness centrality, were analyzed by taking negative correlations among stocks into account. The first 20 stocks in the market networks, termed “major players,” “gatekeeper,” and “vulnerable players,” were explored. On this basis, temporal networks were constructed and the algorithm to test robustness was designed. In addition, quantitative indexes of robustness and evaluation standards of network robustness were introduced and the systematic risks of the stock market were analyzed. This paper enriches the theory on temporal network robustness and provides an effective tool to prevent systematic stock market risks.

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