Understanding the Resilience of Urban Rail Transit: Concepts, Reviews, and Trends
Year: 2024
Type: article
Abstract: 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 data-driven. 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.
Source: Engineering
Authors: Yun Wei, Xin Yang, Xiao Xiao, Zhiao Ma, Tianlei Zhu, Fei Dou, Jianjun Wu, Anthony Chen, Ziyou Gao
Language: English
Subfield: Civil and Structural Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,300
Funders: National Natural Science Foundation of China, Research Grants Council, University Grants Committee, Hong Kong Polytechnic University, Higher Education Discipline Innovation Project, Overseas Expertise Introduction Center for Discipline Innovation of Food Nutrition and Human Health (111 Center)
OpenAlex ID: W4392884053
