Benchmark Analysis for Robustness of Multi-Scale Urban Road Networks Under Global Disruptions

被引:51
|
作者
Shang, Wen-Long [1 ,2 ]
Gao, Ziyou [1 ]
Daina, Nicolo [3 ]
Zhang, Haoran [4 ]
Long, Yin [5 ]
Guo, Zhiling [4 ]
Ochieng, Washington Y. [6 ]
机构
[1] Beijing Jiaotong Univ, Sch Traff & Transportat, Beijing 100044, Peoples R China
[2] Beijing Univ Technol, Coll Metropolitan Transportat, Beijing Key Lab Traff Engn, Beijing 100124, Peoples R China
[3] Univ Strathclyde, Sch Govt & Publ Policy, Glasgow G1 1XQ, Lanark, Scotland
[4] Univ Tokyo, Ctr Spatial Informat Sci, Chiba 2778568, Japan
[5] Univ Tokyo, Grad Sch Engn, Tokyo 1138656, Japan
[6] Imperial Coll London, Ctr Transport Studies, London SW7 2AZ, England
基金
中国国家自然科学基金;
关键词
Robustness; Roads; Benchmark testing; Indexes; Network topology; Planning; Topology; Benchmark analysis; global disruptions; immunity; robustness; urban road networks; ATTACK TOLERANCE; LINKS; METRO; INDEX; ERROR;
D O I
10.1109/TITS.2022.3149969
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
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.
引用
收藏
页码:15344 / 15354
页数:11
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