IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i11p8626-d1156008.html
   My bibliography  Save this article

Simulation of Single-File Pedestrian Flow under High-Density Condition by a Modified Social Force Model

Author

Listed:
  • Cheng-Jie Jin

    (Jiangsu Key Laboratory of Urban ITS, Southeast University of China, Nanjing 210096, China
    Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Nanjing 210096, China)

  • Ke-Da Shi

    (Jiangsu Key Laboratory of Urban ITS, Southeast University of China, Nanjing 210096, China
    Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Nanjing 210096, China)

  • Shu-Yi Fang

    (Jiangsu Key Laboratory of Urban ITS, Southeast University of China, Nanjing 210096, China
    Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Nanjing 210096, China)

Abstract

In this paper, a new modified social force model is proposed to simulate the single-file pedestrian flow at high densities. Since the pedestrians could only follow the preceding person in the single-file flow, the way in which the pedestrian chooses their destination is changed. It is set as the current position of the preceding pedestrian, rather than as one fixed location. In order to simulate the possible movement at high densities, the distance for calculating forces between pedestrians was reset, and the obstacles were divided into many particles. Next, the values of many model parameters were reset, and the ranges of possible parameters were discussed. Furthermore, the data from one large-scale single-file experiment were used for model validations. The simulation results of the fundamental diagrams, spatiotemporal diagrams and the time–headway distributions show that the new model can simulate the single-file movement well. The angular trajectories can help in understanding more about the simulation results. The comparisons between the statistical results of local flow rates and local densities show that, in most cases, the simulated and experimental results are quantitatively similar. This model could be a good choice for the high-density simulations of single-file pedestrian flow.

Suggested Citation

  • Cheng-Jie Jin & Ke-Da Shi & Shu-Yi Fang, 2023. "Simulation of Single-File Pedestrian Flow under High-Density Condition by a Modified Social Force Model," Sustainability, MDPI, vol. 15(11), pages 1-15, May.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:11:p:8626-:d:1156008
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/11/8626/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/15/11/8626/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Haghani, Milad & Sarvi, Majid, 2018. "Crowd behaviour and motion: Empirical methods," Transportation Research Part B: Methodological, Elsevier, vol. 107(C), pages 253-294.
    2. Ebony Carter & Patrick Adam & Deon Tsakis & Stephanie Shaw & Richard Watson & Peter Ryan, 2020. "Enhancing pedestrian mobility in Smart Cities using Big Data," Journal of Management Analytics, Taylor & Francis Journals, vol. 7(2), pages 173-188, April.
    3. Yuan Tang & Yu Xue & Muyang Huang & Qiyun Wen & Bingling Cen & Dong Chen, 2023. "A Lattice Hydrodynamic Model for Four-Way Pedestrian Traffic with Turning Capacity," Sustainability, MDPI, vol. 15(3), pages 1-17, January.
    4. Li, Yang & Chen, Maoyin & Dou, Zhan & Zheng, Xiaoping & Cheng, Yuan & Mebarki, Ahmed, 2019. "A review of cellular automata models for crowd evacuation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 526(C).
    5. Sticco, I.M. & Frank, G.A. & Dorso, C.O., 2021. "Social Force Model parameter testing and optimization using a high stress real-life situation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 561(C).
    6. Zhang, Dawei & Zhu, Haitao & Hostikka, Simo & Qiu, Shi, 2019. "Pedestrian dynamics in a heterogeneous bidirectional flow: Overtaking behaviour and lane formation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 72-84.
    7. Jin, Cheng-Jie & Jiang, Rui & Li, Ruiwen & Li, Dawei, 2019. "Single-file pedestrian flow experiments under high-density conditions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 531(C).
    8. Qu, Yunchao & Xiao, Yao & Wu, Jianjun & Tang, Tao & Gao, Ziyou, 2018. "Modeling detour behavior of pedestrian dynamics under different conditions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 492(C), pages 1153-1167.
    9. Ye, Rui & Zeng, Yiping & Zeng, Guang & Huang, Zhongyi & Li, Xiaolian & Fang, Zhiming & Song, Weiguo, 2021. "Pedestrian single-file movement on stairs under different motivations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 571(C).
    10. Jin, Cheng-Jie & Jiang, Rui & Liu, Tongfei & Li, Dawei & Wang, Hao & Liu, Xianglong, 2021. "Pedestrian dynamics with different corridor widths: Investigation on a series of uni-directional and bi-directional experiments," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 581(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. Jin, Cheng-Jie & Shi, Ke-Da & Jiang, Rui & Li, Dawei & Fang, Shuyi, 2023. "Simulation of bi-directional pedestrian flow under high densities using a modified social force model," Chaos, Solitons & Fractals, Elsevier, vol. 172(C).
    2. Hu, Xiangmin & Chen, Tao & Deng, Kaifeng & Wang, Guanning, 2023. "Effects of aggressiveness on pedestrian room evacuation using extended cellular automata model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 619(C).
    3. Fang, Shuyi & Jin, Cheng-Jie & Jiang, Rui & Li, Dawei, 2024. "Simulating the bi-directional pedestrian flow under high densities by a floor field cellular automaton model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 638(C).
    4. Tan, Bangkun & Xuan, Chenrui & Xie, Wei & Shi, Meng & Ma, Yi, 2024. "Dynamic characteristics of the sideways movement of pedestrians: An experimental study based on single-file experiments," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 639(C).
    5. Kinateder, Max & Warren, William H., 2021. "Exit choice during evacuation is influenced by both the size and proportion of the egressing crowd," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 569(C).
    6. Kefan Xie & Benbu Liang & Yu Song & Xueqin Dong, 2019. "Analysis of Walking-Edge Effect in Train Station Evacuation Scenarios: A Sustainable Transportation Perspective," Sustainability, MDPI, vol. 11(24), pages 1-16, December.
    7. Wei, Yidong & Hu, Zuoan & Zeng, Tian & Xie, Wei & Ma, Yi, 2023. "Influence of walkway slope on single-file pedestrian flow dynamics: Results from an experimental study," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).
    8. Xue, Shuqi & Shiwakoti, Nirajan, 2023. "A meta-synthesis of experimental studies of pedestrian movement in single-file flow," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).
    9. Jin, Cheng-Jie & Jiang, Rui & Liu, Tongfei & Li, Dawei & Wang, Hao & Liu, Xianglong, 2021. "Pedestrian dynamics with different corridor widths: Investigation on a series of uni-directional and bi-directional experiments," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 581(C).
    10. Stock, Eduardo Velasco & da Silva, Roberto, 2023. "Lattice gas model to describe a nightclub dynamics," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
    11. 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.
    12. Gao, Dong Li & Xie, Wei & Ming Lee, Eric Wai, 2022. "Individual-level exit choice behaviour under uncertain risk," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
    13. Jianlin, Li & Jun, Zhang & Xuehua, Song & Hang, Yu & Xintong, Li & Saizhe, Ding & Weiguo, Song, 2024. "The validation of pedestrian trajectories during turning and obstacle avoidance in virtual environments," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 633(C).
    14. Liu, Zhichen & Li, Ying & Zhang, Zhaoyi & Yu, Wenbo, 2022. "A new evacuation accessibility analysis approach based on spatial information," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    15. Liu, Jing & Jia, Yang & Mao, Tianlu & Wang, Zhaoqi, 2022. "Modeling and simulation analysis of crowd evacuation behavior under terrorist attack," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
    16. Kayvan Aghabayk & Alireza Soltani & Nirajan Shiwakoti, 2022. "Investigating Pedestrians’ Exit Choice with Incident Location Awareness in an Emergency in a Multi-Level Shopping Complex," Sustainability, MDPI, vol. 14(19), pages 1-21, September.
    17. Arulsamy, Karen & Delaney, Liam, 2022. "The impact of automatic enrolment on the mental health gap in pension participation: Evidence from the UK," Journal of Health Economics, Elsevier, vol. 86(C).
    18. Siqing Shan & Xin Wen & Yigang Wei & Zijin Wang & Yong Chen, 2020. "Intelligent manufacturing in industry 4.0: A case study of Sany heavy industry," Systems Research and Behavioral Science, Wiley Blackwell, vol. 37(4), pages 679-690, July.
    19. Fu, Libi & Liu, Yuxing & Shi, Yongqian & Zhao, Yongxiang, 2021. "Dynamics of bidirectional pedestrian flow in a corridor including individuals with disabilities," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 580(C).
    20. Moghari, Somaye & Ghorani, Maryam, 2022. "A symbiosis between cellular automata and dynamic weighted multigraph with application on virus spread modeling," Chaos, Solitons & Fractals, Elsevier, vol. 155(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:15:y:2023:i:11:p:8626-:d:1156008. 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.