Understanding the Resilience of Urban Rail Transit: Concepts, Reviews, and Trends

被引:5
作者
Wei, Yun [1 ]
Yang, Xin [2 ]
Xiao, Xiao [3 ]
Ma, Zhiao [2 ]
Zhu, Tianlei [2 ]
Dou, Fei [1 ]
Wu, Jianjun [2 ]
Chen, Anthony [4 ]
Gao, Ziyou [2 ]
机构
[1] Beijing Mass Transit Railway Operat Corp Ltd, Beijing 100044, Peoples R China
[2] Beijing Jiaotong Univ, Sch Syst Sci, Beijing 100044, Peoples R China
[3] Traff Control Technol Corp Ltd, Beijing 100160, Peoples R China
[4] Hong Kong Polytech Univ, Dept Civil & Environm Engn, Hong Kong 999077, Peoples R China
来源
ENGINEERING | 2024年 / 41卷
基金
中国国家自然科学基金;
关键词
Urban rail transit; Resilience assessment; Resilience improvement; Network disruption; NETWORK RESILIENCE; ROBUSTNESS ASSESSMENT; TRANSPORT-SYSTEMS; VULNERABILITY; METRO; OPTIMIZATION; DISRUPTIONS; MANAGEMENT; REDUCTION; FRAMEWORK;
D O I
10.1016/j.eng.2024.01.022
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
As the scale of urban rail transit (URT) networks expands, the study of URT resilience is essential for safe and efficient operations. This paper presents a comprehensive review of URT resilience and highlights potential trends and directions for future research. First, URT resilience is defined by three primary abilities: absorption, resistance, and recovery, and four properties: robustness, vulnerability, rapidity, and redundancy. Then, the metrics and assessment approaches for URT resilience were summarized. The metrics are divided into three categories: topology-based, characteristic-based, and performance-based, and the assessment methods are divided into four categories: topological, simulation, optimization, and datadriven. Comparisons of various metrics and assessment approaches revealed that the current research trend in URT resilience is increasingly favoring the integration of traditional methods, such as conventional complex network analysis and operations optimization theory, with new techniques like big data and intelligent computing technology, to accurately assess URT resilience. Finally, five potential trends and directions for future research were identified: analyzing resilience based on multisource data, optimizing train diagram in multiple scenarios, accurate response to passenger demand through new technologies, coupling and optimizing passenger and traffic flows, and optimal line design. (c) 2024 THE AUTHORS. Published by Elsevier LTD on behalf of Chinese Academy of Engineering and Higher Education Press Limited Company. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
引用
收藏
页码:7 / 18
页数:12
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