IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i9p3919-d356488.html
   My bibliography  Save this article

Transit-Based Evacuation for Urban Rail Transit Line Emergency

Author

Listed:
  • Bowen Hou

    (College of Transportation, Jilin University, Changchun 130022, China)

  • Yang Cao

    (College of Transportation, Jilin University, Changchun 130022, China)

  • Dongye Lv

    (Jilin Provincial Communications Science Research Institute, Changchun 130012, China)

  • Shuzhi Zhao

    (College of Transportation, Jilin University, Changchun 130022, China)

Abstract

Urban rail systems are the backbone of urban transit networks and are characterized by large passenger volumes, high speeds, punctuality, and low environmental impacts. However, unforeseen events such as rail transit line emergencies can lead to unexpected costs and delays. As a means of disruption management, we divide the decision support system for urban rail transit line emergency situations into two stages—transit-based evacuation and bus bridging management. This paper focuses on the transit-based evacuation under emergency scenarios on a single rail line. The model determines the vehicles and routes within traditional transit systems required to evacuate stranded passengers within a given time window. In addition, the proposed method ensures the reliability of traditional transit systems by considering the operating fleet and reserve fleet in the traditional transit systems. Therefore, the proposed optimization model is established with the objective of maximizing the total number of stranded passengers transferred within the given time window and headway constraint. Herein, we present the optimization model and solution method, and the proposed method is validated. The effectiveness of the proposed control method is evaluated in the Changchun urban transit network. By analyzing stranded passengers at stations under different numbers of vehicles and time periods, the results show that the proposed model can significantly provide routing arrangements to maximize the number of passengers evacuated from stations. The results are useful in the development of emergency evacuation plans to prevent secondary accidents and evacuate stranded passengers during a rail transit line emergency.

Suggested Citation

  • Bowen Hou & Yang Cao & Dongye Lv & Shuzhi Zhao, 2020. "Transit-Based Evacuation for Urban Rail Transit Line Emergency," Sustainability, MDPI, vol. 12(9), pages 1-18, May.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:9:p:3919-:d:356488
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/9/3919/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/12/9/3919/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Lei, Wenjun & Li, Angui & Gao, Ran & Hao, Xinpeng & Deng, Baoshun, 2012. "Simulation of pedestrian crowds’ evacuation in a huge transit terminal subway station," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(22), pages 5355-5365.
    2. Yun Wang & Xuedong Yan & Yu Zhou & Wenyi Zhang, 2016. "A Feeder-Bus Dispatch Planning Model for Emergency Evacuation in Urban Rail Transit Corridors," PLOS ONE, Public Library of Science, vol. 11(9), pages 1-28, September.
    3. Lijie Yu & Yarong Cong & Kuanmin Chen, 2020. "Determination of the Peak Hour Ridership of Metro Stations in Xi’an, China Using Geographically-Weighted Regression," Sustainability, MDPI, vol. 12(6), pages 1-22, March.
    4. 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.
    5. Jian Gang Jin & Kwong Meng Teo & Amedeo R. Odoni, 2016. "Optimizing Bus Bridging Services in Response to Disruptions of Urban Transit Rail Networks," Transportation Science, INFORMS, vol. 50(3), pages 790-804, August.
    6. Zhuangbin Shi & Ning Zhang & Yang Liu & Wei Xu, 2018. "Exploring Spatiotemporal Variation in Hourly Metro Ridership at Station Level: The Influence of Built Environment and Topological Structure," Sustainability, MDPI, vol. 10(12), pages 1-16, December.
    7. Chen Wang & Yanchao Song, 2020. "Fire Evacuation in Metro Stations: Modeling Research on the Effects of Two Key Parameters," Sustainability, MDPI, vol. 12(2), pages 1-11, January.
    8. Pender, Brendan & Currie, Graham & Delbosc, Alexa & Shiwakoti, Nirajan, 2014. "Improving bus bridging responses via satellite bus reserve locations," Journal of Transport Geography, Elsevier, vol. 34(C), pages 202-210.
    9. Shahparvari, Shahrooz & Abbasi, Babak, 2017. "Robust stochastic vehicle routing and scheduling for bushfire emergency evacuation: An Australian case study," Transportation Research Part A: Policy and Practice, Elsevier, vol. 104(C), pages 32-49.
    10. Liang, Shidong & Zhao, Shuzhi & Lu, Chunxiu & Ma, Minghui, 2016. "A self-adaptive method to equalize headways: Numerical analysis and comparison," Transportation Research Part B: Methodological, Elsevier, vol. 87(C), pages 33-43.
    11. Ziqi Wang & Peihan Wen, 2020. "Optimization of a Low-Carbon Two-Echelon Heterogeneous-Fleet Vehicle Routing for Cold Chain Logistics under Mixed Time Window," Sustainability, MDPI, vol. 12(5), pages 1-22, March.
    12. 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.
    13. Wan, Jiahui & Sui, Jie & Yu, Hua, 2014. "Research on evacuation in the subway station in China based on the Combined Social Force Model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 394(C), pages 33-46.
    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. Minjun Kim & Gi-Hyoug Cho, 2020. "Influence of Evacuation Policy on Clearance Time under Large-Scale Chemical Accident: An Agent-Based Modeling," IJERPH, MDPI, vol. 17(24), pages 1-18, December.
    2. Yunes Almansoub & Ming Zhong & Asif Raza & Muhammad Safdar & Abdelghani Dahou & Mohammed A. A. Al-qaness, 2022. "Exploring the Effects of Transportation Supply on Mixed Land-Use at the Parcel Level," Land, MDPI, vol. 11(6), pages 1-28, May.

    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, Yao & An, Kun, 2021. "Integrated optimization of bus bridging routes and timetables for rail disruptions," European Journal of Operational Research, Elsevier, vol. 295(2), pages 484-498.
    2. Tang, Junqing & Xu, Lei & Luo, Chunling & Ng, Tsan Sheng Adam, 2021. "Multi-disruption resilience assessment of rail transit systems with optimized commuter flows," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    3. Liang, Jinpeng & Wu, Jianjun & Qu, Yunchao & Yin, Haodong & Qu, Xiaobo & Gao, Ziyou, 2019. "Robust bus bridging service design under rail transit system disruptions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 132(C), pages 97-116.
    4. 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.
    5. 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).
    6. Ma, Liang & Chen, Bin & Wang, Xiaodong & Zhu, Zhengqiu & Wang, Rongxiao & Qiu, Xiaogang, 2019. "The analysis on the desired speed in social force model using a data driven approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 894-911.
    7. 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.
    8. Zheng, Hankun & Sun, Huijun & Kang, Liujiang & Dai, Peiling & Wu, Jianjun, 2023. "Multi-route coordination for bus systems in response to road disruptions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 179(C).
    9. Zhang, Li & Chen, Tingting & Liu, Zhongshan & Yu, Bin & Wang, Yunpeng, 2024. "Analysis of multi-modal public transportation system performance under metro disruptions: A dynamic resilience assessment framework," Transportation Research Part A: Policy and Practice, Elsevier, vol. 183(C).
    10. Jiateng Yin & Lixing Yang & Andrea D’Ariano & Tao Tang & Ziyou Gao, 2022. "Integrated Backup Rolling Stock Allocation and Timetable Rescheduling with Uncertain Time-Variant Passenger Demand Under Disruptive Events," INFORMS Journal on Computing, INFORMS, vol. 34(6), pages 3234-3258, November.
    11. Kuo, Yong-Hong & Leung, Janny M.Y. & Yan, Yimo, 2023. "Public transport for smart cities: Recent innovations and future challenges," European Journal of Operational Research, Elsevier, vol. 306(3), pages 1001-1026.
    12. 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).
    13. Liping Ge & Stefan Voß & Lin Xie, 2022. "Robustness and disturbances in public transport," Public Transport, Springer, vol. 14(1), pages 191-261, March.
    14. Shang, Hua-Yan & Huang, Hai-Jun & Zhang, Yi-Ming, 2015. "An extended mobile lattice gas model allowing pedestrian step size variable," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 424(C), pages 283-293.
    15. Paulsen, Mads & Rasmussen, Thomas Kjær & Nielsen, Otto Anker, 2021. "Impacts of real-time information levels in public transport: A large-scale case study using an adaptive passenger path choice model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 148(C), pages 155-182.
    16. Rahimi-Golkhandan, Armin & Garvin, Michael J. & Brown, Bryan L., 2019. "Characterizing and measuring transportation infrastructure diversity through linkages with ecological stability theory," Transportation Research Part A: Policy and Practice, Elsevier, vol. 128(C), pages 114-130.
    17. Milan Janić, 2018. "Modelling the resilience of rail passenger transport networks affected by large-scale disruptive events: the case of HSR (high speed rail)," Transportation, Springer, vol. 45(4), pages 1101-1137, July.
    18. Rafidah Md Noor & Nadia Bella Gustiani Rasyidi & Tarak Nandy & Raenu Kolandaisamy, 2020. "Campus Shuttle Bus Route Optimization Using Machine Learning Predictive Analysis: A Case Study," Sustainability, MDPI, vol. 13(1), pages 1-24, December.
    19. Ali Shahabi & Sadigh Raissi & Kaveh Khalili-Damghani & Meysam Rafei, 2021. "Designing a resilient skip-stop schedule in rapid rail transit using a simulation-based optimization methodology," Operational Research, Springer, vol. 21(3), pages 1691-1721, September.
    20. Rudke, Anderson Paulo & Martins, Jorge Alberto & dos Santos, Alex Mota & Silva, Witan Pereira & Caldana, Nathan F. da Silva & Souza, Vinicius A.S. & Alves, Ronaldo Adriano & de Almeida Albuquerque, Ta, 2021. "Spatial and socio-economic analysis of public transport systems in large cities: A case study for Belo Horizonte, Brazil," Journal of Transport Geography, Elsevier, vol. 91(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:gam:jsusta:v:12:y:2020:i:9:p:3919-:d:356488. 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.