Assessing the effects of link-repair sequences on road network resilience

Sohouenou, Philippe Y.R.; Neves, Luis A.C.

Abstract

Disruptions to transport networks are inevitable and detrimental to the functioning of society. Improving the resilience of transport networks to disruptive events has, therefore, a significant impact on society. Although the resilience of a transport system depends on the ability of the network to sustain the consequences of initial disruption (i.e. robustness) and quickly recover its performance (i.e. rapidity), the latter attracted less attention than robustness. The present paper focuses on quantifying the impacts of recovery processes and, more specifically, link-repair strategies on resilience. Several link-repair strategies are compared across a multitude of perturbation scenarios in the well-known Sioux Falls network. The strategies considered include: (i) the optimal (minimising the disruption consequences over the recovery process), (ii) average (representing a recovery process where the disrupted links are repaired in random order), (iii) flow-based (where the links with the highest traffic flow in the undisrupted network are repaired first), and (iv) criticality-based (where the links whose individual failure result in the highest impacts on the system performance are repaired first) recovery. The results of this comparison are subsequently used to evaluate the correlation between robustness and resilience, and characterise the optimal repair strategy.

Citation

Sohouenou, P. Y., & Neves, L. A. (2021). Assessing the effects of link-repair sequences on road network resilience. International Journal of Critical Infrastructure Protection, 34, Article 100448. https://doi.org/10.1016/j.ijcip.2021.100448

• Journal Article Type: Article
• Acceptance Date: Apr 26, 2021
• Online Publication Date: Apr 30, 2021
• Publication Date: 2021-09
• Deposit Date: Jul 1, 2021
• Publicly Available Date: May 1, 2022
• Journal: International Journal of Critical Infrastructure Protection
• Print ISSN: 1874-5482
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 34
• Article Number: 100448
• DOI: https://doi.org/10.1016/j.ijcip.2021.100448
• Keywords: Modelling and Simulation; Information Systems and Management; Safety, Risk, Reliability and Quality; Computer Science Applications
• Public URL: https://nottingham-repository.worktribe.com/output/5749815
• Publisher URL: https://www.sciencedirect.com/science/article/abs/pii/S1874548221000408