IDEAS home Printed from https://ideas.repec.org/a/eee/transe/v190y2024ics1366554524003077.html
   My bibliography  Save this article

A Queue-SEIAR model: Revealing the transmission mechanism of epidemics in a metro line from a meso level

Author

Listed:
  • Wu, Aoping
  • Hu, Lu
  • Li, Dongjie
  • Zhu, Juanxiu
  • Shang, Pan

Abstract

The COVID-19 pandemic has affected communities worldwide. The metro, an essential means of public transportation in many cities, is particularly vulnerable to the spread of the virus due to its limited space and complex passenger flow structure. As the basis of quick and effective management decision-making, it is very important but intractable to accurately and quickly capture the transmission mechanism of epidemics in the metro line. This study addresses this challenge by proposing a meso-level Queue-SEIAR model. The Queue-SEIAR model integrates a feedback queuing network model, which captures the nonlinear stochastic effect of the congestion propagation on passenger mobility dynamics in the metro line, with an extended SEIAR (Susceptible, Exposed, Infected, Asymptomatic, and Recovered) epidemic model in multiple-subgroups situations. The risk of infection within a metro line is measured using the Total Number of Newly Exposed Travelers (TNNET). The Euler’s method is used to solve the Queue-SEIAR model, with time complexity that is independent of passenger volumes and station and train capacities, making it suitable for the analysis and decision-making of large-scale metro lines. The Queue-SEIAR model is validated versus the micro-level agent-based simulation. Numerical experiments reveal some interesting findings: (1) The gap between the Queue-SEIAR model and the benchmark Macro-level Epidemic Model (MEM) in terms of TNNET will become larger with congestion; (2) From the Queue-SEIAR model, low-demand and low-risk stations may become high-risk because of the congestion propagation, while MEM does not reveal such a pattern; (3) There exists a moderate allowed entering proportion, train dwell time, and social distance to minimize the TNNET; (4) The optimal social distance increases as the train headway or travel demand increases; and (5) The epidemic prevention effectiveness of side platforms surpasses that of island platforms.

Suggested Citation

  • Wu, Aoping & Hu, Lu & Li, Dongjie & Zhu, Juanxiu & Shang, Pan, 2024. "A Queue-SEIAR model: Revealing the transmission mechanism of epidemics in a metro line from a meso level," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 190(C).
    • Handle: RePEc:eee:transe:v:190:y:2024:i:c:s1366554524003077
    DOI: 10.1016/j.tre.2024.103716
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1366554524003077
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.tre.2024.103716?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. Lifen Jia & Wei Chen, 2021. "Uncertain SEIAR model for COVID-19 cases in China," Fuzzy Optimization and Decision Making, Springer, vol. 20(2), pages 243-259, June.
    2. Jiancheng Long & Ziyou Gao & Xiaomei Zhao & Aiping Lian & Penina Orenstein, 2011. "Urban Traffic Jam Simulation Based on the Cell Transmission Model," Networks and Spatial Economics, Springer, vol. 11(1), pages 43-64, March.
    3. Abdin, Adam F. & Fang, Yi-Ping & Caunhye, Aakil & Alem, Douglas & Barros, Anne & Zio, Enrico, 2023. "An optimization model for planning testing and control strategies to limit the spread of a pandemic – The case of COVID-19," European Journal of Operational Research, Elsevier, vol. 304(1), pages 308-324.
    4. Qian, Xinwu & Ukkusuri, Satish V., 2021. "Connecting urban transportation systems with the spread of infectious diseases: A Trans-SEIR modeling approach," Transportation Research Part B: Methodological, Elsevier, vol. 145(C), pages 185-211.
    5. James S. Vandergraft, 1983. "A Fluid Flow Model of Networks of Queues," Management Science, INFORMS, vol. 29(10), pages 1198-1208, October.
    6. Li, Shukai & Liu, Ronghui & Gao, Ziyou & Yang, Lixing, 2021. "Integrated train dwell time regulation and train speed profile generation for automatic train operations on high-density metro lines: A distributed optimal control method," Transportation Research Part B: Methodological, Elsevier, vol. 148(C), pages 82-105.
    7. Liu, Jielun & Ong, Ghim Ping & Pang, Vincent Junxiong, 2022. "Modelling effectiveness of COVID-19 pandemic control policies using an Area-based SEIR model with consideration of infection during interzonal travel," Transportation Research Part A: Policy and Practice, Elsevier, vol. 161(C), pages 25-47.
    8. Roberto Cominetti & José Correa & Omar Larré, 2015. "Dynamic Equilibria in Fluid Queueing Networks," Operations Research, INFORMS, vol. 63(1), pages 21-34, February.
    9. Jin, Yu & Wang, Wendi & Xiao, Shiwu, 2007. "An SIRS model with a nonlinear incidence rate," Chaos, Solitons & Fractals, Elsevier, vol. 34(5), pages 1482-1497.
    10. Meead Saberi & Homayoun Hamedmoghadam & Mudabber Ashfaq & Seyed Amir Hosseini & Ziyuan Gu & Sajjad Shafiei & Divya J. Nair & Vinayak Dixit & Lauren Gardner & S. Travis Waller & Marta C. González, 2020. "A simple contagion process describes spreading of traffic jams in urban networks," Nature Communications, Nature, vol. 11(1), pages 1-9, December.
    11. Yin, Jiateng & Tang, Tao & Yang, Lixing & Gao, Ziyou & Ran, Bin, 2016. "Energy-efficient metro train rescheduling with uncertain time-variant passenger demands: An approximate dynamic programming approach," Transportation Research Part B: Methodological, Elsevier, vol. 91(C), pages 178-210.
    12. Gao, Yuan & Kroon, Leo & Schmidt, Marie & Yang, Lixing, 2016. "Rescheduling a metro line in an over-crowded situation after disruptions," Transportation Research Part B: Methodological, Elsevier, vol. 93(PA), pages 425-449.
    13. Hu, Lu & Zhao, Bin & Zhu, Juanxiu & Jiang, Yangsheng, 2019. "Two time-varying and state-dependent fluid queuing models for traffic circulation systems," European Journal of Operational Research, Elsevier, vol. 275(3), pages 997-1019.
    14. Anupriya, & Graham, Daniel J. & Bansal, Prateek & Hörcher, Daniel & Anderson, Richard, 2023. "Optimal congestion control strategies for near-capacity urban metros: Informing intervention via fundamental diagrams," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).
    15. Carolina Osorio & Jana Yamani, 2017. "Analytical and Scalable Analysis of Transient Tandem Markovian Finite Capacity Queueing Networks," Transportation Science, INFORMS, vol. 51(3), pages 823-840, August.
    16. MacGregor Smith, J., 1991. "State-dependent queueing models in emergency evacuation networks," Transportation Research Part B: Methodological, Elsevier, vol. 25(6), pages 373-389, December.
    17. Daganzo, Carlos F., 1994. "The cell transmission model: A dynamic representation of highway traffic consistent with the hydrodynamic theory," Transportation Research Part B: Methodological, Elsevier, vol. 28(4), pages 269-287, August.
    18. Abderrahman Iggidr & Gauthier Sallet & Berge Tsanou, 2012. "Global Stability Analysis of a Metapopulation SIS Epidemic Model," Mathematical Population Studies, Taylor & Francis Journals, vol. 19(3), pages 115-129, July.
    19. Mitchell, David H. & MacGregor Smith, J., 2001. "Topological network design of pedestrian networks," Transportation Research Part B: Methodological, Elsevier, vol. 35(2), pages 107-135, February.
    20. Muren, & Zhang, Shiyuan & Hua, Lianlian & Yu, Bo, 2022. "Peak-easing strategies for urban subway operations in the context of COVID-19 epidemic," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 161(C).
    Full references (including those not matched with items on IDEAS)

    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. Chen, Zebin & Li, Shukai & D’Ariano, Andrea & Yang, Lixing, 2022. "Real-time optimization for train regulation and stop-skipping adjustment strategy of urban rail transit lines," Omega, Elsevier, vol. 110(C).
    2. Zheng, Xiangyu & Huang, Ning & Bai, Ya-nan & Zhang, Xin, 2023. "A traffic-fractal-element-based congestion model considering the uneven distribution of road traffic," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 632(P1).
    3. Jinxiao Duan & Guanwen Zeng & Nimrod Serok & Daqing Li & Efrat Blumenfeld Lieberthal & Hai-Jun Huang & Shlomo Havlin, 2023. "Spatiotemporal dynamics of traffic bottlenecks yields an early signal of heavy congestions," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    4. Hong, Liu & Ye, Bowen & Yan, Han & Zhang, Hui & Ouyang, Min & (Sean) He, Xiaozheng, 2019. "Spatiotemporal vulnerability analysis of railway systems with heterogeneous train flows," Transportation Research Part A: Policy and Practice, Elsevier, vol. 130(C), pages 725-744.
    5. Zhang, Fang & Lu, Jian & Hu, Xiaojian & Meng, Qiang, 2023. "A stochastic dynamic network loading model for mixed traffic with autonomous and human-driven vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 178(C).
    6. Huang, Yeran & Yang, Lixing & Tang, Tao & Gao, Ziyou & Cao, Fang, 2017. "Joint train scheduling optimization with service quality and energy efficiency in urban rail transit networks," Energy, Elsevier, vol. 138(C), pages 1124-1147.
    7. Flötteröd, G. & Osorio, C., 2017. "Stochastic network link transmission model," Transportation Research Part B: Methodological, Elsevier, vol. 102(C), pages 180-209.
    8. Xue, Hongjiao & Jia, Limin & Li, Jian & Guo, Jianyuan, 2022. "Jointly optimized demand-oriented train timetable and passenger flow control strategy for a congested subway line under a short-turning operation pattern," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 593(C).
    9. Zhan, Shuguang & Wong, S.C. & Shang, Pan & Peng, Qiyuan & Xie, Jiemin & Lo, S.M., 2021. "Integrated railway timetable rescheduling and dynamic passenger routing during a complete blockage," Transportation Research Part B: Methodological, Elsevier, vol. 143(C), pages 86-123.
    10. Chen, Zebin & D’Ariano, Andrea & Li, Shukai & Tessitore, Marta Leonina & Yang, Lixing, 2024. "Robust dynamic train regulation integrated with stop-skipping strategy in urban rail networks: An outer approximation based solution method," Omega, Elsevier, vol. 128(C).
    11. MacGregor Smith, J. & Cruz, F.R.B., 2014. "M/G/c/c state dependent travel time models and properties," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 395(C), pages 560-579.
    12. Huang, Wei & Hu, Yang, 2022. "A modified cell transmission model considering queuing characteristics for channelized zone at signalized intersections," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 605(C).
    13. Qi, Jianguo & Yang, Lixing & Di, Zhen & Li, Shukai & Yang, Kai & Gao, Yuan, 2018. "Integrated optimization for train operation zone and stop plan with passenger distributions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 109(C), pages 151-173.
    14. Jiang, Feng & Cacchiani, Valentina & Toth, Paolo, 2017. "Train timetabling by skip-stop planning in highly congested lines," Transportation Research Part B: Methodological, Elsevier, vol. 104(C), pages 149-174.
    15. Lu, Yahan & Yang, Lixing & Yang, Hai & Zhou, Housheng & Gao, Ziyou, 2023. "Robust collaborative passenger flow control on a congested metro line: A joint optimization with train timetabling," Transportation Research Part B: Methodological, Elsevier, vol. 168(C), pages 27-55.
    16. Lu, Zhong-Wen & Xu, Yuan-Hao & Chen, Jie & Hu, Mao-Bin, 2023. "Investigation of traffic-driven epidemic spreading by taxi trip data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 632(P1).
    17. Cacchiani, Valentina & Qi, Jianguo & Yang, Lixing, 2020. "Robust optimization models for integrated train stop planning and timetabling with passenger demand uncertainty," Transportation Research Part B: Methodological, Elsevier, vol. 136(C), pages 1-29.
    18. Wang, Yihui & Zhao, Kangqi & D’Ariano, Andrea & Niu, Ru & Li, Shukai & Luan, Xiaojie, 2021. "Real-time integrated train rescheduling and rolling stock circulation planning for a metro line under disruptions," Transportation Research Part B: Methodological, Elsevier, vol. 152(C), pages 87-117.
    19. Hu, Lu & Jiang, Yangsheng & Zhu, Juanxiu & Chen, Yanru, 2015. "A PH/PH(n)/C/C state-dependent queuing model for metro station corridor width design," European Journal of Operational Research, Elsevier, vol. 240(1), pages 109-126.
    20. Mo, Baichuan & Koutsopoulos, Haris N. & Shen, Zuo-Jun Max & Zhao, Jinhua, 2023. "Robust path recommendations during public transit disruptions under demand uncertainty," Transportation Research Part B: Methodological, Elsevier, vol. 169(C), pages 82-107.

    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:transe:v:190:y:2024:i:c:s1366554524003077. 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: http://www.elsevier.com/wps/find/journaldescription.cws_home/600244/description#description .

    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.