Vulnerability analysis of road network for dangerous goods transportation considering intentional attack: Based on Cellular Automata

Wencheng Huang, Bowen Zhou, Yaocheng Yu and Dezhi Yin

Reliability Engineering and System Safety, 2021, vol. 214, issue C

Abstract: The vulnerability of road network for dangerous goods transportation (RNDGT) under cascading failure considering intentional attack is analyzed. We introduce the time characteristics of load distribution and node recovery ability into previous cascading failure model, subdivide the state of failed node into normal state, partial failure state and complete failure state. Six traffic load distribution strategies including Average Distribution (AD), Betweenness Distribution (BD), Capacity Distribution (CD), Degree Distribution (DD), Tightness Distribution (TD) and Surplus Load Distribution (SLD) are selected to study the load re-distribution of failed nodes. In addition, three kinds of intentional node attack strategies including Degree Attack (DA), Betweenness Attack (BA) and Tightness Attack (TA) are selected to study the impact on the vulnerability. By referring the application of cellular automata applied in epidemic spreading field, we establish a new cascading failure model of RNDGT. The improved maximum connectivity and node failure rate based on node degree are applied to analyze the vulnerability. A case study is conducted by using the RNDGT of Dalian as the background. The previous Motter-Lai model (M-L) is applied as the comparison approach. TA strategy has the least impact on increasing network vulnerability, SLD strategy is the best to reduce network vulnerability.

Keywords: Road network for dangerous goods transportation; Vulnerability analysis; Cascading failure; Cellular Automata; Node recovery ability; Intentional attack (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (16)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:214:y:2021:i:c:s0951832021003045

DOI: 10.1016/j.ress.2021.107779

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