Data-driven resilience analysis of the global container shipping network against two cascading failures
Data-driven resilience analysis of the global container shipping network against two cascading failures
Being a fundamental link in the global supply chain and logistics system, the global container shipping network (GCSN) is highly interconnected, which causes the network resilience challenges by the cascading failures triggered by extreme events (e.g., COVID-19 and regional conflicts). Within this dynamic process, the load redistribution behaviour is the core countermeasure for the propagation of cascading failures, however the diversified mechanism has not been systematically studied. To fill in these gaps, this study aims to develop a pioneering resilience analysis framework against cascading failures, to comprehensively explore the impact of port disruptions on the shipping network resilience. By pioneering the influence analysis of port betweenness, weight, and connectivity on load determination and target selection, a port importance assessment method is applied as the foundation for load redistribution decisions. Based on the global service routes data from 2020 to 2023, the GCSN resilience against the sequential cascading failures of 686 ports worldwide is quantified by three metrics. A scenario analysis is conducted to simulate the effects of cascading failures triggered by 5 historical port disruption events (e.g., the COVID-19 port lockdowns and the 2024 bridge collision at Baltimore port) on resilience of the network. Determining the identified critical capacity threshold is pivotal for effectively enhancing the system's resilience and preventing the likelihood of cascading failures. Additionally, this study offers cutting-edge perspectives to the global shipping industry stakeholders. It presents distinct strategies and preferences, offering actionable advice for port authorities in their risk response decisions. Moreover, this study delivers an economic rationale and critical evaluations, instrumental for the strategic maintenance, planning and augmentation of port infrastructures to prevent unforeseen risks.
Cascading Failures, Global Container Shipping Network, Maritime Transport, Redistribution Rule, Resilience Analysis
Cao, Yuhao
e5d32def-17bf-4bb3-bb57-95385401bc29
Xin, Xuri
ee253afb-c13f-476e-ac8b-4545e7487f90
Jarumaneeroj, Pisit
35a74270-2b3d-43ef-9fb1-47311409d5fa
Li, Huanhuan
5e806b21-10a7-465c-9db3-32e466ae42f1
Feng, Yinwei
6ceb3646-6244-4dd6-b9ee-a4aeb0d03cb2
Wang, Jin
4acf1e64-f0a3-4cd3-9011-82b4682c0927
Wang, Xinjian
f5b36426-10e7-4d48-8798-e34b972b3af0
Pyne, Robyn
b2bb6604-80bd-4e74-8ecb-a3e119892627
Yang, Zaili
82d4eebc-4532-4343-8555-35169e79bb6d
19 November 2024
Cao, Yuhao
e5d32def-17bf-4bb3-bb57-95385401bc29
Xin, Xuri
ee253afb-c13f-476e-ac8b-4545e7487f90
Jarumaneeroj, Pisit
35a74270-2b3d-43ef-9fb1-47311409d5fa
Li, Huanhuan
5e806b21-10a7-465c-9db3-32e466ae42f1
Feng, Yinwei
6ceb3646-6244-4dd6-b9ee-a4aeb0d03cb2
Wang, Jin
4acf1e64-f0a3-4cd3-9011-82b4682c0927
Wang, Xinjian
f5b36426-10e7-4d48-8798-e34b972b3af0
Pyne, Robyn
b2bb6604-80bd-4e74-8ecb-a3e119892627
Yang, Zaili
82d4eebc-4532-4343-8555-35169e79bb6d
Cao, Yuhao, Xin, Xuri, Jarumaneeroj, Pisit, Li, Huanhuan, Feng, Yinwei, Wang, Jin, Wang, Xinjian, Pyne, Robyn and Yang, Zaili
(2024)
Data-driven resilience analysis of the global container shipping network against two cascading failures.
Transportation Research Part E: Logistics and Transportation Review, 193, [103857].
(doi:10.1016/j.tre.2024.103857).
Abstract
Being a fundamental link in the global supply chain and logistics system, the global container shipping network (GCSN) is highly interconnected, which causes the network resilience challenges by the cascading failures triggered by extreme events (e.g., COVID-19 and regional conflicts). Within this dynamic process, the load redistribution behaviour is the core countermeasure for the propagation of cascading failures, however the diversified mechanism has not been systematically studied. To fill in these gaps, this study aims to develop a pioneering resilience analysis framework against cascading failures, to comprehensively explore the impact of port disruptions on the shipping network resilience. By pioneering the influence analysis of port betweenness, weight, and connectivity on load determination and target selection, a port importance assessment method is applied as the foundation for load redistribution decisions. Based on the global service routes data from 2020 to 2023, the GCSN resilience against the sequential cascading failures of 686 ports worldwide is quantified by three metrics. A scenario analysis is conducted to simulate the effects of cascading failures triggered by 5 historical port disruption events (e.g., the COVID-19 port lockdowns and the 2024 bridge collision at Baltimore port) on resilience of the network. Determining the identified critical capacity threshold is pivotal for effectively enhancing the system's resilience and preventing the likelihood of cascading failures. Additionally, this study offers cutting-edge perspectives to the global shipping industry stakeholders. It presents distinct strategies and preferences, offering actionable advice for port authorities in their risk response decisions. Moreover, this study delivers an economic rationale and critical evaluations, instrumental for the strategic maintenance, planning and augmentation of port infrastructures to prevent unforeseen risks.
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Accepted/In Press date: 5 November 2024
e-pub ahead of print date: 19 November 2024
Published date: 19 November 2024
Keywords:
Cascading Failures, Global Container Shipping Network, Maritime Transport, Redistribution Rule, Resilience Analysis
Identifiers
Local EPrints ID: 503704
URI: http://eprints.soton.ac.uk/id/eprint/503704
ISSN: 1366-5545
PURE UUID: 1d591824-4ccd-401c-95b0-835a90480019
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Date deposited: 11 Aug 2025 16:36
Last modified: 22 Aug 2025 02:49
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Contributors
Author:
Yuhao Cao
Author:
Xuri Xin
Author:
Pisit Jarumaneeroj
Author:
Huanhuan Li
Author:
Yinwei Feng
Author:
Jin Wang
Author:
Xinjian Wang
Author:
Robyn Pyne
Author:
Zaili Yang
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