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Conference Papers Year : 2019

Adapting the Social Force Model for Low Density Crowds in Open Environments

Abstract

The Social Force Model (SFM) successfully reproduces many collective phenomena in evacuations or dense crowds. However, pedestrians behaviour is context dependent and the SFM has some limitations when simulating crowds in an open environment under normal conditions. Specifically, in an urban public square pedestrians tend to expand their personal space and try to avoid dense areas to reduce the risk of collision. Based on the SFM, the proposed model splits the perception of pedestrians into a large perception zone and a restricted frontal zone to which they pay more attention. Through their perceptions, the agents estimate the crowd density and dynamically adapt their personal space. Finally, the original social force is tuned to reflect pedestrians preference of avoiding dense areas by turning rather than slowing down as long as there is enough space. Simulation results show that in the considered context the proposed approach produces more realistic behaviours than the original SFM. The simulated crowd is less dense with the same number of pedestrians and less collisions occur, which better fits the observations of sparse crowds in an open place under normal conditions.
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Dates and versions

hal-02194735 , version 1 (25-07-2019)

Identifiers

  • HAL Id : hal-02194735 , version 1

Cite

Manon Prédhumeau, Julie Dugdale, Anne Spalanzani. Adapting the Social Force Model for Low Density Crowds in Open Environments. 15th Social Simulation Conference, Sep 2019, Mainz, Germany. ⟨hal-02194735⟩
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