Benchmark analysis for robustness of multi-scale urban road networks under global disruptions

Shang, Wen-Long and Gao, Ziyou and Daina, Nicolò and Zhang, Haoran and Long, Yin and Guo, Zhiling and Ochieng, Washington Y. (2022) Benchmark analysis for robustness of multi-scale urban road networks under global disruptions. IEEE Transactions on Intelligent Transportation Systems. ISSN 1558-0016 (https://doi.org/10.1109/TITS.2022.3149969)

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Abstract

To date immunity to disruptions of multi-scale urban road networks (URNs) has not been effectively quantified. This study uses robustness as a meaningful - if partial - representation of immunity. We propose a novel Relative Area Index (RAI) based on traffic assignment theory to quantitatively measure the robustness of URNs under global capacity degradation due to three different types of disruptions, which takes into account many realistic characteristics. We also compare the RAI with weighted betweenness centrality, a traditional topological metric of robustness. We employ six realistic URNs as case studies for this comparison. Our analysis shows that RAI is a more effective measure of the robustness of URNs when multi-scale URNs suffer from global disruptions. This improved effectiveness is achieved because of RAI's ability to capture the effects of realistic network characteristics such as network topology, flow patterns, link capacity, and travel demand. Also, the results highlight the importance of central management when URNs suffer from disruptions. Our novel method may provide a benchmark tool for comparing robustness of multi-scale URNs, which facilitates the understanding and improvement of network robustness for the planning and management of URNs.

ORCID iDs

Shang, Wen-Long, Gao, Ziyou, Daina, Nicolò ORCID logoORCID: https://orcid.org/0000-0002-5902-4651, Zhang, Haoran, Long, Yin, Guo, Zhiling and Ochieng, Washington Y.;
CORE (COnnecting REpositories)