Title: Using a random road graph model to understand road networks robustness to link failures
Authors: SOHOUENOU PHILIPPECHRISTIDIS PANAYOTISCHRISTODOULOU ARISNEVES LUIS A.C.LO PRESTI DAVIDE
Citation: INTERNATIONAL JOURNAL OF CRITICAL INFRASTRUCTURE PROTECTION vol. 29 p. 100353
Publisher: ELSEVIER SCIENCE BV
Publication Year: 2020
JRC N°: JRC118493
ISSN: 1874-5482 (online)
URI: https://publications.jrc.ec.europa.eu/repository/handle/JRC118493
DOI: 10.1016/j.ijcip.2020.100353
Type: Articles in periodicals and books
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.
JRC Directorate:Energy, Transport and Climate

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