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Social Force Model parameter testing and optimization using a high stress real-life situation

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  • Sticco, I.M.
  • Frank, G.A.
  • Dorso, C.O.

Abstract

The escape panic version of the Social Force Model (SFM) is a suitable model for describing emergency evacuations. In this research, we analyze a real-life video, recorded at the opening of a store during a Black Friday event, which resembles an emergency evacuation (November 2017, South Africa). We measure the flow of pedestrians entering the store and found a higher value (〈J〉=6.7±0.8p/s) than the usually reported in “laboratory” conditions. We performed numerical simulations to recreate this event. The empirical measurements were compared against simulated evacuation curves corresponding to different sets of parameters currently in use in the literature. The results obtained suggest that the set of parameters corresponding to calibrations from laboratory experiments (involving pedestrians in which the safety of the participants is of major concern) or situations where the physical contact is negligible, produce simulations in which the agents evacuate faster than in the empirical scenario. To conclude the paper, we optimize two parameters of the model: the friction coefficient kt and the body force coefficient kn. The best fit we found could replicate the qualitative and quantitative behavior of the empirical evacuation curve. We also found that many different combinations in the parameter space can produce similar results in terms of the goodness of fit.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:phsmap:v:561:y:2021:i:c:s0378437120306853
    DOI: 10.1016/j.physa.2020.125299
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    References listed on IDEAS

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    3. García, Ander & Hernández-Delfin, Dariel & González, Borja & Garitaonaindia, Germán & Lee, Dae-Jin & Ellero, Marco, 2024. "Analysis of local density during football stadium access: Integrating pedestrian flow simulations and empirical data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 638(C).
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    5. 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.
    6. 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.
    7. Cornes, F.E. & Frank, G.A. & Dorso, C.O., 2021. "Microscopic dynamics of the evacuation phenomena in the context of the Social Force Model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 568(C).
    8. García, Ander & Hernández-Delfin, Dariel & Lee, Dae-Jin & Ellero, Marco, 2023. "Limited visual range in the Social Force Model: Effects on macroscopic and microscopic dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 612(C).
    9. Cui, Geng & Yanagisawa, Daichi & Nishinari, Katsuhiro, 2023. "Learning from experimental data to simulate pedestrian dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 623(C).
    10. Zhou, Zi-Xuan & Nakanishi, Wataru & Asakura, Yasuo, 2021. "Data-driven framework for the adaptive exit selection problem in pedestrian flow: Visual information based heuristics approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 583(C).

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