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Limited visual range in the Social Force Model: Effects on macroscopic and microscopic dynamics

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  • García, Ander
  • Hernández-Delfin, Dariel
  • Lee, Dae-Jin
  • Ellero, Marco

Abstract

The Social Force Model has been widely used to simulate pedestrian dynamics. Its simplicity and ability to reproduce some collective patterns of behavior make it an adequate tool in the field of pedestrian dynamics. However, its ability to reproduce common macroscopic empirical results, such as pedestrian flows through a bottleneck and the speed-density fundamental diagram, has scarcely been studied. In addition, the effect of each parameter of the model on the dynamics of the system has rarely been shown. In this contribution, a comprehensive parameter-sensitivity analysis in the social force model is provided, and an optimal set is introduced, capable of reproducing both macroscopic experimental flow data and collision avoidance between pedestrians in simple trajectories on the microscopic scale. We show that the incorporation of asymmetric visual range models in the inter-pedestrian interactions is required for quantitative agreement. The model is also capable of showing collision avoidance in simple pedestrian trajectories and lane formation in non-crowded bidirectional pedestrian flows.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:phsmap:v:612:y:2023:i:c:s037843712300016x
    DOI: 10.1016/j.physa.2023.128461
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    References listed on IDEAS

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    1. Anders Johansson & Dirk Helbing & Pradyumn K. Shukla, 2007. "Specification Of The Social Force Pedestrian Model By Evolutionary Adjustment To Video Tracking Data," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 10(supp0), pages 271-288.
    2. Dirk Helbing & Illés Farkas & Tamás Vicsek, 2000. "Simulating dynamical features of escape panic," Nature, Nature, vol. 407(6803), pages 487-490, September.
    3. Nagai, Ryoichi & Fukamachi, Masahiro & Nagatani, Takashi, 2006. "Evacuation of crawlers and walkers from corridor through an exit," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 367(C), pages 449-460.
    4. 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).
    5. Parisi, Daniel R. & Gilman, Marcelo & Moldovan, Herman, 2009. "A modification of the Social Force Model can reproduce experimental data of pedestrian flows in normal conditions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(17), pages 3600-3608.
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    Cited by:

    1. 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).
    2. Zhihai, Tang & Longcheng, Yang & Jun, Hu & Xiaoning, Li & Lei, You, 2024. "An improved social force model for improving pedestrian avoidance by reducing search size," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 643(C).
    3. Tian, Jiangtao & Li, Xingli & Guo, Qinghua & Kuang, Hua, 2024. "Dynamics characteristic of pedestrians’ particular overtaking behavior based on an improved social force model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 643(C).

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