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Video-driven group behavior simulation based on social comparison theory

Author

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  • Zhang, Jianxin
  • Liu, Hong
  • Li, Yan
  • Qin, Xin
  • Wang, Shouna

Abstract

In simulation studies of crowd behaviors, group behavioral modeling is the core issue of crowd simulation evacuation research. The core issue in group behavioral modeling is how to construct the group. This paper proposes examining the dissimilarities between pedestrians by tracing the trajectories of pedestrians from real videos and constructing groups under real scenes based on the dissimilarities between individuals according to the principle of common fate. The inter-group dissimilarity and intra-group dissimilarity are calculated by pedestrians’ trajectories and groups obtained. We present an improved social force model by the social comparison theory and intra-group attraction strategy combined with the data from video. In this paper, the scene information, the locations of the pedestrians, the groups of pedestrians, the intra-group dissimilarity and the inter-group dissimilarity are extracted from the real video. In addition, we considered intra-group attraction and the characteristic of the social comparison theory that lead pedestrians to gather together in emergency. Therefore, the method proposed in this paper has higher authenticity. To verify the effectiveness of the proposed method, we qualitatively and quantitatively designed comparative experiments, respectively. The experimental results show that the method can simulate the group behavior and reproduce the evacuation of people in emergency in real scenes. This model can serve as an important tool to predict evacuation statuses in different population densities and group distributions in emergency. It provides a basis for the establishment of a system to track pedestrians and predict their congestion.

Suggested Citation

  • Zhang, Jianxin & Liu, Hong & Li, Yan & Qin, Xin & Wang, Shouna, 2018. "Video-driven group behavior simulation based on social comparison theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 620-634.
  • Handle: RePEc:eee:phsmap:v:512:y:2018:i:c:p:620-634
    DOI: 10.1016/j.physa.2018.08.046
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    References listed on IDEAS

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    Cited by:

    1. Shi Sun & Cheng Sun & Dorine C. Duives & Serge P. Hoogendoorn, 2023. "Neural network model for predicting variation in walking dynamics of pedestrians in social groups," Transportation, Springer, vol. 50(3), pages 837-868, June.
    2. Subramanian, Gayathri Harihara & Choubey, Nipun & Verma, Ashish, 2022. "Modelling and simulating serpentine group behaviour in crowds using modified social force model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
    3. Ding, Ning & Zhu, Yu & Liu, Xinyan & Dong, Dapeng & Wang, Yang, 2024. "A modified social force model for crowd evacuation considering collision predicting behaviors," Applied Mathematics and Computation, Elsevier, vol. 466(C).

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