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Impact Estimation of Unplanned Urban Rail Disruptions on Public Transport Passengers: A Multi-Agent Based Simulation Approach

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  • Chengli Cong

    (School of Maritime and Transportation, Ningbo University, Ningbo 315211, China)

  • Xuan Li

    (School of Maritime and Transportation, Ningbo University, Ningbo 315211, China
    Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Road #2, Nanjing 211189, China)

  • Shiwei Yang

    (School of Maritime and Transportation, Ningbo University, Ningbo 315211, China)

  • Quan Zhang

    (School of Maritime and Transportation, Ningbo University, Ningbo 315211, China)

  • Lili Lu

    (School of Maritime and Transportation, Ningbo University, Ningbo 315211, China
    Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Road #2, Nanjing 211189, China)

  • Yang Shi

    (Ningbo Urban Planning & Design Institute, Ningbo 315042, China)

Abstract

Once unplanned urban rail disruptions occur, it is essential to evaluate the impacts on public transport passengers since impact estimation results enable transit agencies to verify whether alternative transit services have adequate capacity to evacuate the affected rail passengers and to adopt effective emergency measures in response to the disruptions. This paper focuses on estimating the impacts of unplanned rail line segment disruptions on rail passengers as well as original bus passengers, as the latter are overlooked in existing studies. A method of identifying affected rail passengers based on passenger tap-in time is proposed, which is helpful for evaluating the scale and origin-destination distribution of the affected passengers. Passengers’ response behaviors are analyzed and modeled in a multi-agent simulation system. The system realizes the simulation of the multimodal evacuation process, in which a rule-based logit model is employed to describe passengers’ travel selection behavior and the Monte Carlo method is utilized to address the issue of uncertainty in passengers’ travel selection. In particular, the original bus passengers are integrated into the simulation and interact with rail passengers. Finally, some indicators assessing the impacts on rail passengers and bus passengers are presented, and a case study based on the Ningbo urban rail transit network is conducted.

Suggested Citation

  • Chengli Cong & Xuan Li & Shiwei Yang & Quan Zhang & Lili Lu & Yang Shi, 2022. "Impact Estimation of Unplanned Urban Rail Disruptions on Public Transport Passengers: A Multi-Agent Based Simulation Approach," IJERPH, MDPI, vol. 19(15), pages 1-25, July.
  • Handle: RePEc:gam:jijerp:v:19:y:2022:i:15:p:9052-:d:871331
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    1. Xingchuan Wang & Enjian Yao & Shasha Liu, 2018. "Travel Choice Analysis under Metro Emergency Context: Utility? Regret? Or Both?," Sustainability, MDPI, vol. 10(11), pages 1-15, October.
    2. Sun, Daniel (Jian) & Guan, Shituo, 2016. "Measuring vulnerability of urban metro network from line operation perspective," Transportation Research Part A: Policy and Practice, Elsevier, vol. 94(C), pages 348-359.
    3. Guimarães, Vanessa de Almeida & Leal Junior, Ilton Curty & da Silva, Marcelino Aurélio Vieira, 2018. "Evaluating the sustainability of urban passenger transportation by Monte Carlo simulation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 93(C), pages 732-752.
    4. Li, Binbin & Yao, Enjian & Yamamoto, Toshiyuki & Tang, Ying & Liu, Shasha, 2020. "Exploring behavioral heterogeneities of metro passenger’s travel plan choice under unplanned service disruption with uncertainty," Transportation Research Part A: Policy and Practice, Elsevier, vol. 141(C), pages 294-306.
    5. Sun, Huijun & Wu, Jianjun & Wu, Lijuan & Yan, Xiaoyong & Gao, Ziyou, 2016. "Estimating the influence of common disruptions on urban rail transit networks," Transportation Research Part A: Policy and Practice, Elsevier, vol. 94(C), pages 62-75.
    6. Chen, Jingxu & Liu, Zhiyuan & Zhu, Senlai & Wang, Wei, 2015. "Design of limited-stop bus service with capacity constraint and stochastic travel time," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 83(C), pages 1-15.
    7. Sun, Lishan & Huang, Yuchen & Chen, Yanyan & Yao, Liya, 2018. "Vulnerability assessment of urban rail transit based on multi-static weighted method in Beijing, China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 108(C), pages 12-24.
    8. Chen, Jinqu & Liu, Jie & Peng, Qiyuan & Yin, Yong, 2022. "Resilience assessment of an urban rail transit network: A case study of Chengdu subway," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 586(C).
    9. Bhat, Chandra R., 1998. "Accommodating variations in responsiveness to level-of-service measures in travel mode choice modeling," Transportation Research Part A: Policy and Practice, Elsevier, vol. 32(7), pages 495-507, September.
    10. Li, Yang & Yang, Xin & Wu, Jianjun & Sun, Huijun & Guo, Xin & Zhou, Li, 2021. "Discrete-event simulations for metro train operation under emergencies: A multi-agent based model with parallel computing," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 573(C).
    11. Jin, Jian Gang & Tang, Loon Ching & Sun, Lijun & Lee, Der-Horng, 2014. "Enhancing metro network resilience via localized integration with bus services," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 63(C), pages 17-30.
    12. Mo, Baichuan & Koutsopoulos, Haris N. & Zhao, Jinhua, 2022. "Inferring passenger responses to urban rail disruptions using smart card data: A probabilistic framework," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 159(C).
    13. Leng, Nuannuan & Corman, Francesco, 2020. "The role of information availability to passengers in public transport disruptions: An agent-based simulation approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 133(C), pages 214-236.
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    Cited by:

    1. Hexin Hu & Jitao Li & Shuai Wu, 2022. "Simulation Evaluation of a Current Limiting Scheme in an Urban Rail Transit Network," Sustainability, MDPI, vol. 15(1), pages 1-18, December.
    2. Xinyu Zhuang & Li Zhang & Jie Lu, 2022. "Past—Present—Future: Urban Spatial Succession and Transition of Rail Transit Station Zones in Japan," IJERPH, MDPI, vol. 19(20), pages 1-35, October.

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