Using a random road graph model to understand road networks robustness to link failures

Sohouenou, Philippe Y.R.; Christidis, Panayotis; Christodoulou, Aris; Neves, Luis A.C.; Presti, Davide Lo

Abstract

Disruptions to the transport system have a greater impact on society and the economy now than ever before due to the increased interconnectivity and interdependency of the economic sectors. The ability of transport systems to maintain functionality despite various disturbances (i.e. robustness) is hence of tremendous importance and has been the focus of research seeking to support transport planning, design and management. These approaches and findings may nevertheless be only valid for the specific networks studied. The present study attempts to find universal insights into road networks robustness by exploring the correlation between different network attributes and network robustness to single, multiple, random and targeted link failures. For this purpose, the common properties of road graphs were identified through a literature review. On this basis, the GREREC model was developed to randomly generate a variety of abstract networks presenting the topological and operational characteristics of real-road networks, on which a robustness analysis was performed. This analysis quantifies the difference between the link criticality rankings when only single-link failures are considered as opposed to when multiple-link failures are considered and the difference between the impact of targeted and random attacks. The influence of the network attributes on the network robustness and on these two differences is shown and discussed. Finally, this analysis is also performed on a set of real road networks to validate the results obtained with the artificial networks.

Citation

Sohouenou, P. Y., Christidis, P., Christodoulou, A., Neves, L. A., & Presti, D. L. (2020). Using a random road graph model to understand road networks robustness to link failures. International Journal of Critical Infrastructure Protection, 29, Article 100353. https://doi.org/10.1016/j.ijcip.2020.100353

• Journal Article Type: Article
• Acceptance Date: Mar 7, 2020
• Online Publication Date: Apr 29, 2020
• Publication Date: 2020-06
• Deposit Date: May 17, 2020
• Publicly Available Date: May 19, 2020
• Journal: International Journal of Critical Infrastructure Protection
• Print ISSN: 1874-5482
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 29
• Article Number: 100353
• DOI: https://doi.org/10.1016/j.ijcip.2020.100353
• Keywords: Random road network; Network robustness; Resilience assessment; Link criticality; Targeted attacks; Graph theory
• Public URL: https://nottingham-repository.worktribe.com/output/4458833
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S1874548220300172
• Additional Information: This article is maintained by: Elsevier; Article Title: Using a random road graph model to understand road networks robustness to link failures; Journal Title: International Journal of Critical Infrastructure Protection; CrossRef DOI link to publisher maintained version: https://doi.org/10.1016/j.ijcip.2020.100353; Content Type: article; Copyright: © 2020 The Authors. Published by Elsevier B.V.