Recovery Strategies for Urban Rail Transit Network Based on Comprehensive Resilience

Mingming Zheng (), Hanzhang Zuo, Zitong Zhou and Yuhan Bai
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Mingming Zheng: School of Traffic and Transportation Engineering, Dalian Jiaotong University, Dalian 116028, China
Hanzhang Zuo: School of Environment and Chemical Engineering, Dalian Jiaotong University, Dalian 116028, China
Zitong Zhou: School of Traffic and Transportation Engineering, Dalian Jiaotong University, Dalian 116028, China
Yuhan Bai: Zhejiang Haining Rail Transit Operation Management Co., Jiaxing 314411, China

Sustainability, 2023, vol. 15, issue 20, 1-17

Abstract: To enhance the resilience of urban rail transit networks in dealing with interference events and facilitating rapid network recovery, this paper focuses on studying damaged urban rail transit networks and proposes comprehensive resilience evaluation indexes for urban rail transit networks that take into account two dimensions: network topology and passenger travel path selection. A bi-level programming model is constructed to maximize the comprehensive toughness, where the upper-level model is an integer planning model for determining the optimal recovery sequence of the affected stations under interference events that result in station closure or inoperability. The lower-level model is a passenger flow allocation model aiming to minimize travelers’ impedance. A genetic algorithm and Dijkstra’s labeling algorithm are used to solve the upper model as well as the shortest path of the lower model, respectively. Using a real-world urban rail transit network as an example, this research applies different recovery strategies, random recovery, node importance-based recovery, and comprehensive toughness-based recovery, across five common interference scenarios to analyze the recovery sequence of stations in each scenario. The modeling results show that the comprehensive toughness-based restoration strategy yields the most favorable results for the rail transportation network, followed by the node importance-based restoration strategy. In addition, the network’s toughness varies more significantly when employing different restoration strategies during target interference, as compared to the random and range interference scenarios.

Keywords: urban rail transit; resilience; bi-level programming; genetic algorithm (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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