An improved social force model (ISFM)-based crowd evacuation simulation method in virtual reality with a subway fire as a case study

Zhang, Jinbin; Zhu, Jun; Dang, Pei; Wu, Jianlin; Zhou, Yuxuan; Li, Weilian; Lin, Fu‐Cheng; Guo, Yukun; You, Jigang

doi:10.1080/17538947.2023.2197261

Cited by 28 publications

(7 citation statements)

References 60 publications

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“…These methods abstract crowd motion into mathematical equations or deterministic systems, demonstrating excellent scalability and robustness. They are applicable to various tasks, including pedestrian simulation [7], high-density crowd simulation [13], and crowd evacuation simulation [3,4], among others. However, these homogeneous behavior models may not fully capture the complexity of crowd behavior, resulting in limitations in achieving realism.…”

Section: Rule-based Crowd Simulation Methodsmentioning

confidence: 99%

“…Crowd simulation, crucial in computer graphics and system modeling, plays a key role in applications that extensively use electronic systems and technologies. These include urban modeling [1,2], emergency evacuation planning [3][4][5][6][7], game design [8], and behavior analysis [9,10]. This paper introduces an innovative method for crowd simulation that integrates deep learning with the traditional social force model.…”

Section: Introductionmentioning

confidence: 99%

“…Many of these methods have achieved outstanding results in specific areas. For instance, the optimal reciprocal collision avoidance algorithm (ORCA) [11] excels in collision avoidance, and improved algorithms based on the classical social force model [12] have demonstrated remarkable performance in tasks such as high-density crowd simulation [13] and evacuation simulation [3,4]. However, human behavior is inherently complex, and representing the intricate dynamics of crowds solely through homogeneous rules or physical calculations can be challenging.…”

Section: Introductionmentioning

confidence: 99%

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Enhanced Crowd Dynamics Simulation with Deep Learning and Improved Social Force Model

Yan,

Ding,

Huang

et al. 2024

Electronics

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The traditional social force model (SFM) in crowd simulation experiences difficulty coping with the complexity of the crowd, limited by singular physical formulas and parameters. Recent attempts to combine deep learning with these models focus more on simulating specific states of crowds. This paper introduces an advanced deep social force model, influenced by crowd states. It utilizes deep neural networks to accurately fit crowd trajectory features, enhancing behavior simulation capabilities. Geometrical constraints within the model provide control over varied crowd behaviors, adjustable to simulate different crowd types. Before training, we use the SFM to refine behaviors in real trajectories with excessively small distances, aiming to enhance the general applicability of the model. Comparative experiments affirm the effectiveness of the model, showing comparable performance to both classic physical models and modern learning-based hybrid models in pedestrian simulations, with reduced collisions. In addition, the model has a certain ability to simulate crowds with high density and diverse behaviors.

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Section: Rule-based Crowd Simulation Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Enhanced Crowd Dynamics Simulation with Deep Learning and Improved Social Force Model

Yan,

Ding,

Huang

et al. 2024

Electronics

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“…Visualization methods enhance human understanding of complex information by displaying data features in an intuitive and attractive way [29][30][31][32][33]. In order to improve users' accurate understanding of this key information when conveying diagnostic information about tunnel excavation deformation, this information must be transformed into easyto-understand visual variables [34][35][36][37][38]. The digital twin scenes of tunnel excavation are complex and changeable.…”

Section: Multivariate Visual Variable Integration In the Digital Twin...mentioning

confidence: 99%

A Knowledge-Guided Intelligent Analysis Method of Geographic Digital Twin Models: A Case Study on the Diagnosis of Geometric Deformation in Tunnel Excavation Profiles

Liang,

Zhu,

Zhang

et al. 2024

IJGI

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It is essential to establish a digital twin scene, which helps to depict the dynamically changing geographical environment accurately. Digital twins could improve the refined management level of intelligent tunnel construction; however, research on geographical twin models primarily focuses on modeling and visual description, which has low analysis efficiency. This paper proposes a knowledge-guided intelligent analysis method for the geometric deformation of tunnel excavation profile twins. Firstly, a dynamic data-driven knowledge graph of tunnel excavation twin scenes was constructed to describe tunnel excavation profile twin scenes accurately. Secondly, an intelligent diagnosis algorithm for geometric deformation of tunnel excavation contour twins was designed by knowledge guidance. Thirdly, multiple visual variables were jointly used to support scene fusion visualization of tunnel excavation profile twin scenes. Finally, a case was selected to implement the experimental analysis. The experimental results demonstrate that the method in this article can achieve an accurate description of objects and their relationships in tunnel excavation twin scenes, which supports rapid geometric deformation analysis of the tunnel excavation profile twin. The speed of geometric deformation diagnosis is increased by more than 90% and the cognitive efficiency is improved by 70%. The complexity and difficulty of the deformation analysis operation are reduced, and the diagnostic analysis ability and standardization of the geographic digital twin model are effectively improved.

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“…Model-based simulations are often the main research methodologies used to analyze crowd evacuation instead of real-world experiments, which are highly challenging to examine crowd behavior during an emergency evacuation [3][4][5]. The simulation models in this case are driven by environmental information, which should be qualitative instead of quantitative.…”

Section: Introductionmentioning

confidence: 99%

Agent-Based Simulation of Crowd Evacuation Through Complex Spaces

Chatra,

Bourahla

2024

ISI

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In this paper, we have developed a description of an agent-based model for simulating the evacuation of crowds from complex physical spaces for escaping dangerous situations. The model describes a physical space containing a set of differently shaped fences, and obstacles, and an exit door. The pedestrians comprising the crowd and moving in this space in order to be evacuated are described as intelligent agents with supervised machine learning using perception-based data to perceive a particular environment differently. The description of this model is developed with the Python language where its execution represents its simulation. Before the simulation, the model can be validated using an animation written with the same language to fix possible problems in the model description. A model performance evaluation is presented using an analysis of simulation results, showing that these results are very encouraging.

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An improved social force model (ISFM)-based crowd evacuation simulation method in virtual reality with a subway fire as a case study

Cited by 28 publications

References 60 publications

Enhanced Crowd Dynamics Simulation with Deep Learning and Improved Social Force Model

Enhanced Crowd Dynamics Simulation with Deep Learning and Improved Social Force Model

A Knowledge-Guided Intelligent Analysis Method of Geographic Digital Twin Models: A Case Study on the Diagnosis of Geometric Deformation in Tunnel Excavation Profiles

Agent-Based Simulation of Crowd Evacuation Through Complex Spaces

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