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Resilience-based optimization model for emergency bus bridging and dispatching in response to metro operational disruptions

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  • Jiefei Zhang
  • Gang Ren
  • Jianhua Song

Abstract

Bus-bridging evacuation services can significantly enhance metro resilience during operational disruptions. A resilience-based optimization model was proposed to generate a bus bridging and dispatching plan. The objective of the model is to maximize the resilience index of evacuated passengers while meeting pre-established restrictions on operational indicators and resources. The proposed approach consists of three steps: representing an integrated network based on a hyper-network, generating candidate bus-bridging routes using the K-shortest paths algorithm, and solving the optimization model using a genetic algorithm to determine the optimal vehicle allocation among the candidate routes. The Nanjing metro network was used to demonstrate the proposed model. The results show that the average waiting time is the main reason for travel delays, especially in short-distance travel. Furthermore, the cycling strategy is beneficial for reducing the average travel delay and improving evacuation efficiency with limited vehicles. In particular, when resources are very limited, the vehicle cycling strategy may have significant advantages over fixed vehicles for servicing fixed lines. The proposed model could be widely used in emergency response to quickly and efficiently evacuate passengers.

Suggested Citation

  • Jiefei Zhang & Gang Ren & Jianhua Song, 2023. "Resilience-based optimization model for emergency bus bridging and dispatching in response to metro operational disruptions," PLOS ONE, Public Library of Science, vol. 18(3), pages 1-22, March.
  • Handle: RePEc:plo:pone00:0277577
    DOI: 10.1371/journal.pone.0277577
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    References listed on IDEAS

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    1. Yajuan Deng & Xiaolei Ru & Ziqi Dou & Guohua Liang, 2018. "Design of Bus Bridging Routes in Response to Disruption of Urban Rail Transit," Sustainability, MDPI, vol. 10(12), pages 1-17, November.
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