Resilience-Based Restoration Sequence Optimization for Metro Networks: A Case Study in China

Zhang, Jiefei; Ren, Gang; Song, Jiwen

doi:10.1155/2022/8595356

Cited by 8 publications

(10 citation statements)

References 26 publications

(49 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Chen et al [13] used the average loss ratio of time-related performance indicators to evaluate the resilience of urban rail transit networks and proposed a simulation-based resilience evaluation flow chart. Zhang [14] et al put forward a resilience-based optimization model for choosing an optimal restoration sequence scheme. Zheng et al [15] proposed a comprehensive resilience evaluation index for URT networks to ensure the sustainability of URT networks.…”

Section: Literature Reviewmentioning

confidence: 99%

Simulation-Based Resilience Evaluation for Urban Rail Transit Transfer Stations

Yin,

Chen,

2024

Sustainability

View full text Add to dashboard Cite

Disturbances often occur in transfer stations; however, little is known about the weaknesses of transfer stations and their ability to cope with passenger flows. Therefore, this paper introduces resilience into the study of transfer stations to enhance their emergency response processes and improve the sustainability of URT networks. It establishes a two-level fuzzy evaluation model, using the G1 weighting method, to assess resilience across various scenarios (daily operation, heavy passenger flow, and emergencies) and identify weaknesses; then, corresponding enhancement strategies are proposed. First, factor sets are established according to resilience stages, including rapidity before disturbance, robustness, redundancy, resourcefulness, and rapidity after disturbance. Using the G1 method, the weight matrix for each factor is calibrated, and a membership degree matrix is determined based on their affiliation with the review set. Multiplying the weight matrix and membership degree matrix yields the resilience value. We apply these steps to a representative station with the assistance of Anylogic simulation in calculating the hard-to-obtain data, yielding a peak-hour resilience value of 0.3425, which indicates a “poor” rating in the review set. By combining the peak-hour resilience with resilience curves under different multiples of peak-hour flows, an enhancement prioritization strategy is proposed for the station, which can act as a reference for the management of URT transfer stations.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Simulation-Based Resilience Evaluation for Urban Rail Transit Transfer Stations

Yin,

Chen,

2024

Sustainability

View full text Add to dashboard Cite

show abstract

“…Many robot decision-making architectures and index and optimization techniques [52] Te authors proposed a resilience-based optimization model in order to evaluate and select an optimal restoration sequence scheme, maximizing the global average efciency of metro networks of China under the contingency of network accessibility meeting resilience requirements. Evolutionary algorithm NSGA-II is applied to solve the said model.…”

Section: Referencementioning

confidence: 99%

Evaluating a Sustainable Intelligent Logistic System (ILS) Utilizing O-S Data and Holistic Managerial Models

Liu,

Rao,

Yogi

et al. 2023

Journal of Advanced Transportation

View full text Add to dashboard Cite

The intelligent logistic system (ILS) has benefited Industry 4.0. ILS assures clean, on-time manufacturing. Due to its unique architectures, qualities, and sensory aspects, the robot logistic system (RLS) is sought after as an ILS in Industry 4.0. In case of COVID-19, multiple nations routinely used RLS as ILS to cleanse areas, check patients, and monitor crowds on highways. Research documents (RDs) show that prior researchers attempted to build the static robot logistic system (RLS) performance mapping index; however, most indexes measured only anatomy performance of static RLS. Thus, few RDs are edited previously in the context of MRLS. It is sensed that few RDs examined MRLS-linked performance mapping indexes, including only regular subjective (S) or objective (O) designs, excluding mixed S-O architectures. Most RDs constantly execute the linguistic variables related to fuzzy, grey, rough, ambiguous, and intuitionistic sets/scales to tackle the uncertainty connected with MRLS designs. The authors prioritize those as RGs. The authors proposed (1) an MRLS performance mapping index with respect to technical, cost, and value O-S architectures for recruiting MRLS, (2) linear information to assign ratings in a range of min-max values choosing from 1 to 100% without executing the linguistic scale, and (3) Holistic Managerial Models (HMM-1 and HMM-2) to handle subjective ratings and significance, assigned by Ex against evaluated O-S architectures under linear scale (1–100%). To prove the concept, RLS performance mapping is shown. Only MRLS recruitment and selection are covered. The effort helps CIM, FMS, and WCMS create sustainable, cleaner operations and achieve future goals.

show abstract

“…Considering three dimensions of resistance, recovery, and adaptation and combined with ordered binary comparison, entropy weight, and cloud model, Jiao et al [33] developed a model for evaluating metro stations' resilience levels against rainstorm disasters and used Chongqing metro system as a case study. Taking both budget limitations and repair time uncertainty into account, Zhang et al [34] proposed a resilience-based optimization model for choosing an optimal restoration sequence scheme, maximizing the global average efficiency, under the condition that the network accessibility meets given resilience requirements. To model the causal relationships to quantify the importance of different risks to the overall resilience criteria, Yin et al [35] proposed a hybrid knowledge-based and data-driven approach for the quantitative analysis of resilience.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Influence Analysis of the Important Station Evolution on the Resilience of Complex Metro Network

Meng¹,

Zhao

Liu

et al. 2023

Sustainability

View full text Add to dashboard Cite

With the flourishing development of the urban metro system, the topology of important nodes changes as the metro network structure evolves further. The identical important node has distinct impacts on various metro networks’ resilience. At present, the dynamic influences of important station evolution on the resilience of metro networks remain to be studied further. Taking Shenzhen Metro Network (SZMN) as an example, the dynamic influences of the structure evolution of important nodes on the resilience of the metro network were investigated in this study. Firstly, the dynamic evolution characteristics of complex network topology and node centralities in metro systems were mined. Then, combined with the node interruption simulation and the resilience loss triangle theory, the resilience levels of distinct metro networks facing the failure of the same critical node were statistically assessed. Additionally, suggestions for optimal network recovery strategies for diverse cases were made. Finally, based on the evaluation results of node importance and network resilience, the dynamic influences of the topological evolution of important nodes on the resilience of metro networks were thoroughly discussed. The study’s findings help us comprehend the metro network’s development features better and can assist the metro management department in making knowledgeable decisions and taking appropriate action in an emergency. This study has theoretical and practical significance for the resilient operation and sustainable planning of urban metro network systems.

show abstract

Resilience-Based Restoration Sequence Optimization for Metro Networks: A Case Study in China

Cited by 8 publications

References 26 publications

Simulation-Based Resilience Evaluation for Urban Rail Transit Transfer Stations

Simulation-Based Resilience Evaluation for Urban Rail Transit Transfer Stations

Evaluating a Sustainable Intelligent Logistic System (ILS) Utilizing O-S Data and Holistic Managerial Models

Dynamic Influence Analysis of the Important Station Evolution on the Resilience of Complex Metro Network

Contact Info

Product

Resources

About