IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v219y2022ics095183202100689x.html
   My bibliography  Save this article

Adaptive multi-objective optimization for emergency evacuation at metro stations

Author

Listed:
  • Guo, Kai
  • Zhang, Limao

Abstract

Evacuation is a critical issue at metro stations, where damage, or even death, could be caused due to unexpected accidents if without proper evacuation. Multi-objectives are often desired in the evacuation management, and evacuation strategies should be tailored due to the dynamic features of overcrowded passengers and high uncertainty at the metro stations. A simulation-based approach integrating Light Gradient Boosting Machine (LightGBM) and Non-dominated Sorting Genetic Algorithm III (NSGA-III) is proposed to realize the automatic evacuation evaluation and adaptive optimization at metro stations. A framework consisting of 3 objectives and 9 influential factors is developed for the evacuation evaluation and adaptive optimization. A LightGBM based meta-model is used to construct the relationship between influential factors and objectives. A LightGBM and NSGA-III integrated optimization algorithm is employed to automatically evaluate the evacuation events and seek the adaptive strategies for the station renovation in order to achieve a safe evacuation under specific conditions. A station model simulating a metro station in Singapore is constructed to test the effectiveness and applicability of the proposed approach. It is found that (1) Among 50 target station based cases, 33 failing cases (at least one of the objectives fails) are identified, and the automatic evaluation results indicate the studied metro station could successfully evacuate the passengers when the passenger volume is no more than 1000, but it is very likely to fail when a higher volume exists; (2) Adaptive optimization strategies can be found for every different scenario, and an average 24.8% of the improvement degree can be achieved for all scenarios; (3) The adaptive multi-objective optimization is more cost-effective, presenting an average cost efficiency 2.04, which is significantly higher than the average of the non-adaptive optimization cost efficiency, 0.57. The novelty of this research lies in that (a) A LightGBM-based meta-model is built to construct the relationship between desired multi-objectives and the influential factors, which lays the foundation for the evacuation management from multi-objective optimization perspective; (b) The LightGBM and NSGA-III integrated model can realize the automatic evacuation evaluation and accordingly provide the adaptive strategies under specific conditions.

Suggested Citation

  • Guo, Kai & Zhang, Limao, 2022. "Adaptive multi-objective optimization for emergency evacuation at metro stations," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    • Handle: RePEc:eee:reensy:v:219:y:2022:i:c:s095183202100689x
    DOI: 10.1016/j.ress.2021.108210
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S095183202100689X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2021.108210?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Zhang, Yihao & Chai, Zhaojie & Lykotrafitis, George, 2021. "Deep reinforcement learning with a particle dynamics environment applied to emergency evacuation of a room with obstacles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 571(C).
    2. Byun, Ji-Eun & Song, Junho, 2020. "Efficient probabilistic multi-objective optimization of complex systems using matrix-based Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 200(C).
    3. Lv, Y. & Yan, X.D. & Sun, W. & Gao, Z.Y., 2015. "A risk-based method for planning of bus–subway corridor evacuation under hybrid uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 139(C), pages 188-199.
    4. Han, Yanbin & Liu, Hong, 2017. "Modified social force model based on information transmission toward crowd evacuation simulation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 499-509.
    5. Zhang, Limao & Lin, Penghui, 2021. "Multi-objective optimization for limiting tunnel-induced damages considering uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Hu, Jie & Wen, Weiping & Zhai, Changhai & Pei, Shunshun, 2024. "Post-earthquake functionality assessment for urban subway systems: Incorporating the combined effects of seismic performance of structural and non-structural systems and functional interdependencies," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    2. Liu, Enze & Barker, Kash & Chen, Hong, 2022. "A multi-modal evacuation-based response strategy for mitigating disruption in an intercity railway system," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    3. Zhang, Yan & Teoh, Bak Koon & Wu, Maozhi & Chen, Jiayu & Zhang, Limao, 2023. "Data-driven estimation of building energy consumption and GHG emissions using explainable artificial intelligence," Energy, Elsevier, vol. 262(PA).
    4. Wen, Tao & Gao, Qiuya & Chen, Yu-wang & Cheong, Kang Hao, 2022. "Exploring the vulnerability of transportation networks by entropy: A case study of Asia–Europe maritime transportation network," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    5. Chen, Weiyi & Zhang, Limao, 2022. "An automated machine learning approach for earthquake casualty rate and economic loss prediction," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    6. Liu, Qiong & Guo, Kai & Wu, Xianguo & Xiao, Zhonghua & Zhang, Limao, 2024. "Simulation-based rescue plan modeling and performance assessment towards resilient metro systems under emergency," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    7. Liu, Qiong & He, Renfei & Zhang, Limao, 2022. "Simulation-based multi-objective optimization for enhanced safety of fire emergency response in metro stations," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    8. Zheng, Shuai & Liu, Yugang & Lin, Yexin & Wang, Qiang & Yang, Hongtai & Chen, Bin, 2022. "Bridging strategy for the disruption of metro considering the reliability of transportation system: Metro and conventional bus network," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    9. Zhou, Jianfeng & Reniers, Genserik, 2022. "Petri-net based cooperation modeling and time analysis of emergency response in the context of domino effect prevention in process industries," Reliability Engineering and System Safety, Elsevier, vol. 223(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Xianing Wang & Zhan Zhang & Ying Wang & Jun Yang & Linjun Lu, 2022. "A Study on Safety Evaluation of Pedestrian Flows Based on Partial Impact Dynamics by Real-Time Data in Subway Stations," Sustainability, MDPI, vol. 14(16), pages 1-19, August.
    2. Changxi Ma & Jibiao Zhou & Dong Yang, 2020. "Causation Analysis of Hazardous Material Road Transportation Accidents Based on the Ordered Logit Regression Model," IJERPH, MDPI, vol. 17(4), pages 1-25, February.
    3. Ji, Yanjie & Gao, Liangpeng & Chen, Dandan & Ma, Xinwei & Zhang, Ruochen, 2018. "How does a static measure influence passengers’ boarding behaviors and bus dwell time? Simulated evidence from Nanjing bus stations," Transportation Research Part A: Policy and Practice, Elsevier, vol. 110(C), pages 13-25.
    4. Bemah Ibrahim & Isaac Ahenkorah & Anthony Ewusi, 2022. "Explainable Risk Assessment of Rockbolts’ Failure in Underground Coal Mines Based on Categorical Gradient Boosting and SHapley Additive exPlanations (SHAP)," Sustainability, MDPI, vol. 14(19), pages 1-16, September.
    5. Feng, Liuyang & Zhang, Limao, 2022. "Enhanced prediction intervals of tunnel-induced settlement using the genetic algorithm and neural network," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    6. Liu, Wenli & Li, Ang & Fang, Weili & Love, Peter E.D. & Hartmann, Timo & Luo, Hanbin, 2023. "A hybrid data-driven model for geotechnical reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    7. Byun, Ji-Eun & Song, Junho, 2021. "Generalized matrix-based Bayesian network for multi-state systems," Reliability Engineering and System Safety, Elsevier, vol. 211(C).
    8. Jiaying Qin & Sasa Ma & Lei Zhang & Qianling Wang & Guoce Feng, 2022. "Modeling and Simulation for Non-Motorized Vehicle Flow on Road Based on Modified Social Force Model," Mathematics, MDPI, vol. 11(1), pages 1-18, December.
    9. Zhang, Ke & Lin, Xi & Li, Meng, 2023. "Graph attention reinforcement learning with flexible matching policies for multi-depot vehicle routing problems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 611(C).
    10. Kai Yu & Nannan Qu & Jifeng Lu & Lujie Zhou, 2022. "Determining Subway Emergency Evacuation Efficiency Using Hybrid System Dynamics and Multiple Agents," Mathematics, MDPI, vol. 10(19), pages 1-18, October.
    11. Zheng, Shuai & Liu, Yugang & Lin, Yexin & Wang, Qiang & Yang, Hongtai & Chen, Bin, 2022. "Bridging strategy for the disruption of metro considering the reliability of transportation system: Metro and conventional bus network," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    12. Zhang, Yan & Teoh, Bak Koon & Wu, Maozhi & Chen, Jiayu & Zhang, Limao, 2023. "Data-driven estimation of building energy consumption and GHG emissions using explainable artificial intelligence," Energy, Elsevier, vol. 262(PA).
    13. Yongqing Guo & Siyuan Ma & Fulu Wei & Liqun Lu & Feng Sun & Jie Wang, 2022. "Analysis of Behavior Characteristics for Pedestrian Twice-Crossing at Signalized Intersections Based on an Improved Social Force Model," Sustainability, MDPI, vol. 14(4), pages 1-17, February.
    14. He, Jun, 2021. "An extended recursive decomposition algorithm for dynamic seismic reliability evaluation of lifeline networks with dependent component failures," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    15. Can Liao & Kejun Zhu & Haixiang Guo & Jian Tang, 2019. "Simulation Research on Safe Flow Rate of Bidirectional Crowds Using Bayesian-Nash Equilibrium," Complexity, Hindawi, vol. 2019, pages 1-15, January.
    16. Yang, Xiaoxia & Yang, Xiaoli & Xue, Shuqi & Zhang, Jihui & Pan, Fuquan & Kang, Yuanlei & Wang, Qianling, 2019. "The effect of waiting area design at the metro platform on passengers’ alighting and boarding behaviors," Applied Mathematics and Computation, Elsevier, vol. 358(C), pages 177-193.
    17. Sun, Yutong & Liu, Hong, 2021. "Crowd evacuation simulation method combining the density field and social force model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 566(C).
    18. Oluwatuyi, Opeyemi E. & Ng, Kam & Wulff, Shaun S., 2023. "Improved resistance prediction and reliability for bridge pile foundation in shales through optimal site investigation plans," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
    19. Qu, Pengfei & Zhang, Limao & Zhu, Qizhi & Wu, Maozhi, 2023. "Probabilistic reliability assessment of twin tunnels considering fluid–solid coupling with physics-guided machine learning," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    20. Shirgir, Sina & Shamsaddinlou, Amir & Zare, Reza Najafi & Zehtabiyan, Sorour & Bonab, Masoud Hajialilue, 2023. "An efficient double-loop reliability-based optimization with metaheuristic algorithms to design soil nail walls under uncertain condition," Reliability Engineering and System Safety, Elsevier, vol. 232(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:reensy:v:219:y:2022:i:c:s095183202100689x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.