IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i2p1128-d1027853.html
   My bibliography  Save this article

Collaborative Determination Method of Metro Train Plan Adjustment and Passenger Flow Control under the Impact of COVID-19

Author

Listed:
  • Fuquan Pan

    (School of Civil Engineering, Qingdao University of Technology, Qingdao 266520, China)

  • Jingshuang Li

    (School of Civil Engineering, Qingdao University of Technology, Qingdao 266520, China)

  • Hailiang Tang

    (Qingdao Metro Operation Co., Ltd., Qingdao 266100, China)

  • Changxi Ma

    (School of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou 730070, China)

  • Lixia Zhang

    (School of Civil Engineering, Qingdao University of Technology, Qingdao 266520, China)

  • Xiaoxia Yang

    (School of Civil Engineering, Qingdao University of Technology, Qingdao 266520, China)

Abstract

Aiming at the problem of metro operation and passenger transport organization under the impact of the novel coronavirus (COVID-19), a collaborative determination method of train planning and passenger flow control is proposed to reduce the train load rate in each section and decrease the risk of spreading COVID-19. The Fisher optimal division method is used to determine reasonable passenger flow control periods, and based on this, different flow control rates are adopted for each control period to reduce the difficulty of implementing flow control at stations. According to the actual operation and passenger flow changes, a mathematical optimization model is established. Epidemic prevention risk values (EPRVs) are defined based on the standing density criteria for trains to measure travel safety. The optimization objectives of the model are to minimize the EPRV of trains in each interval, the passenger waiting time and the operating cost of the corporation. The decision variables are the number of running trains during the study period and the flow control rate at each station. The original model is transformed into a single-objective model by the linear weighting of the target, and the model is solved by designing a particle swarm optimization and genetic algorithm (PSO-GA). The validity of the method and the model is verified by actual metro line data. The results of the case study show that when a line is in the moderate-risk area of COVID-19, two more trains should be added to the full-length and short-turn routes after optimization. Combined with the flow control measures for large passenger flow stations, the maximum train load rate is reduced by 35.18%, and the load rate of each section of trains is less than 70%, which meets the requirements of COVID-19 prevention and control. The method can provide a theoretical basis for related research on ensuring the safety of metro operation during COVID-19.

Suggested Citation

  • Fuquan Pan & Jingshuang Li & Hailiang Tang & Changxi Ma & Lixia Zhang & Xiaoxia Yang, 2023. "Collaborative Determination Method of Metro Train Plan Adjustment and Passenger Flow Control under the Impact of COVID-19," Sustainability, MDPI, vol. 15(2), pages 1-17, January.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:2:p:1128-:d:1027853
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/2/1128/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/2/1128/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hongjiao Xue & Limin Jia & Jianyuan Guo, 2020. "Adaptive Multilevel Collaborative Passenger Flow Control in Peak Hours for a Subway Line," Advances in Mathematical Physics, Hindawi, vol. 2020, pages 1-16, September.
    2. Liu, Renming & Li, Shukai & Yang, Lixing, 2020. "Collaborative optimization for metro train scheduling and train connections combined with passenger flow control strategy," Omega, Elsevier, vol. 90(C).
    3. Li, Shukai & Dessouky, Maged M. & Yang, Lixing & Gao, Ziyou, 2017. "Joint optimal train regulation and passenger flow control strategy for high-frequency metro lines," Transportation Research Part B: Methodological, Elsevier, vol. 99(C), pages 113-137.
    4. Aghabayk, Kayvan & Esmailpour, Javad & Shiwakoti, Nirajan, 2021. "Effects of COVID-19 on rail passengers’ crowding perceptions," Transportation Research Part A: Policy and Practice, Elsevier, vol. 154(C), pages 186-202.
    5. Jie Xu & Yao Ning & Heng Wei & Wei Xie & Jianyuan Guo & Limin Jia & Yong Qin, 2015. "Route Choice in Subway Station during Morning Peak Hours: A Case of Guangzhou Subway," Discrete Dynamics in Nature and Society, Hindawi, vol. 2015, pages 1-8, March.
    6. Jianyuan Guo & Limin Jia & Yong Qin & Huijuan Zhou, 2015. "Cooperative Passenger Inflow Control in Urban Mass Transit Network with Constraint on Capacity of Station," Discrete Dynamics in Nature and Society, Hindawi, vol. 2015, pages 1-7, October.
    7. Yang, Yun & Ma, Changxi & Ling, Gang, 2022. "Pre-location for temporary distribution station of urban emergency materials considering priority under COVID-19: A case study of Wuhan City, China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 597(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. 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).
    2. Di, Zhen & Yang, Lixing & Shi, Jungang & Zhou, Housheng & Yang, Kai & Gao, Ziyou, 2022. "Joint optimization of carriage arrangement and flow control in a metro-based underground logistics system," Transportation Research Part B: Methodological, Elsevier, vol. 159(C), pages 1-23.
    3. Zhang, Huimin & Li, Shukai & Wang, Yihui & Yang, Lixing & Gao, Ziyou, 2021. "Collaborative real-time optimization strategy for train rescheduling and track emergency maintenance of high-speed railway: A Lagrangian relaxation-based decomposition algorithm," Omega, Elsevier, vol. 102(C).
    4. 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).
    5. 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).
    6. 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.
    7. Zhang, Ping & Sun, Huijun & Qu, Yunchao & Yin, Haodong & Jin, Jian Gang & Wu, Jianjun, 2021. "Model and algorithm of coordinated flow controlling with station-based constraints in a metro system," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 148(C).
    8. Yuan, Yin & Li, Shukai & Yang, Lixing & Gao, Ziyou, 2022. "Real-time optimization of train regulation and passenger flow control for urban rail transit network under frequent disturbances," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 168(C).
    9. Li, Shukai & Liu, Ronghui & Yang, Lixing & Gao, Ziyou, 2019. "Robust dynamic bus controls considering delay disturbances and passenger demand uncertainty," Transportation Research Part B: Methodological, Elsevier, vol. 123(C), pages 88-109.
    10. Yung-Tsan Jou & Charmine Sheena Saflor & Klint Allen Mariñas & Michael Nayat Young, 2023. "Determining Factors Affecting Perceived Customer Satisfaction on Public Utility Bus System in Occidental Mindoro, Philippines: A Case Study on Service Quality Assessment during Major Disruptions," Sustainability, MDPI, vol. 15(4), pages 1-21, February.
    11. Wang, Dian & D’Ariano, Andrea & Zhao, Jun & Zhong, Qingwei & Peng, Qiyuan, 2022. "Integrated rolling stock deadhead routing and timetabling in urban rail transit lines," European Journal of Operational Research, Elsevier, vol. 298(2), pages 526-559.
    12. Liang, Jinpeng & Wu, Jianjun & Gao, Ziyou & Sun, Huijun & Yang, Xin & Lo, Hong K., 2019. "Bus transit network design with uncertainties on the basis of a metro network: A two-step model framework," Transportation Research Part B: Methodological, Elsevier, vol. 126(C), pages 115-138.
    13. Esmailpour, Javad & Aghabayk, Kayvan & Aghajanzadeh, Mohammad & De Gruyter, Chris, 2022. "Has COVID-19 changed our loyalty towards public transport? Understanding the moderating role of the pandemic in the relationship between service quality, customer satisfaction and loyalty," Transportation Research Part A: Policy and Practice, Elsevier, vol. 162(C), pages 80-103.
    14. Linghui Xu & Jia Lu & Shuichao Zhang & Gang Ren & Kangkang He, 2024. "Subway Multi-Station Coordinated Dynamic Control Method Considering Transfer Inbound Passenger Flow," Sustainability, MDPI, vol. 16(24), pages 1-22, December.
    15. 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.
    16. Mo, Pengli & D’Ariano, Andrea & Yang, Lixing & Veelenturf, Lucas P. & Gao, Ziyou, 2021. "An exact method for the integrated optimization of subway lines operation strategies with asymmetric passenger demand and operating costs," Transportation Research Part B: Methodological, Elsevier, vol. 149(C), pages 283-321.
    17. Mašek Jaroslav & Pálková Adriana & Blaho Peter & Halajová Štefánia & Jursová Simona & Šipuš Denis, 2023. "Proposal for Using IT Solutions in Public Passenger Transport in Slovak Republic to Reduce the Spread of COVID-19," LOGI – Scientific Journal on Transport and Logistics, Sciendo, vol. 14(1), pages 181-191, January.
    18. Pezoa, Raúl & Basso, Franco & Quilodrán, Paulina & Varas, Mauricio, 2023. "Estimation of trip purposes in public transport during the COVID-19 pandemic: The case of Santiago, Chile," Journal of Transport Geography, Elsevier, vol. 109(C).
    19. Zhichao Cao & Yuqing Wang & Zihao Yang & Changjun Chen & Silin Zhang, 2023. "Timetable Rescheduling Using Skip-Stop Strategy for Sustainable Urban Rail Transit," Sustainability, MDPI, vol. 15(19), pages 1-29, October.
    20. Shang, Pan & Li, Ruimin & Guo, Jifu & Xian, Kai & Zhou, Xuesong, 2019. "Integrating Lagrangian and Eulerian observations for passenger flow state estimation in an urban rail transit network: A space-time-state hyper network-based assignment approach," Transportation Research Part B: Methodological, Elsevier, vol. 121(C), pages 135-167.

    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:gam:jsusta:v:15:y:2023:i:2:p:1128-:d:1027853. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.