scholarly journals Visual information based social force model for crowd evacuation

2022 ◽  
Vol 27 (3) ◽  
pp. 619-629
Author(s):  
Wenhan Wu ◽  
Maoyin Chen ◽  
Jinghai Li ◽  
Binglu Liu ◽  
Xiaolu Wang ◽  
...  
Keyword(s):  
Visual Information ◽  
Force Model ◽  
Social Force ◽  
Social Force Model ◽  
Crowd Evacuation

miSFM: On combination of Mutual Information and Social Force Model towards simulating crowd evacuation

2015 ◽  
Vol 168 ◽  
pp. 529-537 ◽  
Author(s):  
Mingliang Xu ◽  
Yunpeng Wu ◽  
Pei Lv ◽  
Hao Jiang ◽  
Mingxuan Luo ◽  
...  
Keyword(s):  
Mutual Information ◽  
Force Model ◽  
Social Force ◽  
Social Force Model ◽  
Crowd Evacuation

scholarly journals Simulating Crowd Evacuation in a Social Force Model with Iterative Extended State Observer

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Juan Wei ◽  
Wenjie Fan ◽  
Zhongyu Li ◽  
Yangyong Guo ◽  
Yuanyuan Fang ◽  
...  
Keyword(s):  
State Observer ◽  
Extended State Observer ◽  
Force Model ◽  
Social Force ◽  
Extended State ◽  
Social Force Model ◽  
External Interference ◽  
The Social ◽  
Crowd Evacuation ◽  
Improved Model

Due to the interaction and external interference, the crowds will constantly and dynamically adjust their evacuation path in the evacuation process to achieve the purpose of rapid evacuation. The information from previous process can be used to modify the current evacuation control information to achieve a better evacuation effect, and iterative learning control can achieve an effective prediction of the expected path within a limited running time. In order to depict this process, the social force model is improved based on an iterative extended state observer so that the crowds can move along the optimal evacuation path. First, the objective function of the optimal evacuation path is established in the improved model, and an iterative extended state observer is designed to get the estimated value. Second, the above model is verified through simulation experiments. The results show that, as the number of iterations increases, the evacuation time shows a trend of first decreasing and then increasing.

scholarly journals Crowd evacuation simulation for walking-along-side effect in the Stadium

2020 ◽  
Vol 309 ◽  
pp. 05001
Author(s):  
Benbu Liang ◽  
Kefan Xie ◽  
Xueqin Dong
Keyword(s):  
Side Effects ◽  
Large Mass ◽  
Force Model ◽  
Mass Gathering ◽  
Social Force ◽  
Social Force Model ◽  
Evacuation Simulation ◽  
Agent Simulation ◽  
Multi Agent ◽  
Crowd Evacuation

With growing concerns about stadiums where attract large mass gathering, modeling and simulating crowd evacuation is pertinent to ensuring efficient and safe conditions. Based on the modified social force model and multi-agent simulation, several simulation scenarios are conducted to study the walking-along-side effects. The results show that walking along the sides will increase evacuation time, but it can mitigate the pressure of clogging effects and stream arching queue. Meanwhile, walking-along-side effects can relieve the density pressure of the exit and the "fast-is-slow" phenomenon. At last, several suggestions are put forward to promote evacuating capacity of the stadium.

Simulation of Crowd Evacuation in Laboratory using Social Force Model

2018 ◽  
Author(s):  
Fatin Syazwani Ismail ◽  
Shamsul Mohamad ◽  
Khalid Isa ◽  
Shaharil Mohd Shah ◽  
Rafizah Mohd Hanifa ◽  
...  
Keyword(s):  
Force Model ◽  
Social Force ◽  
Social Force Model ◽  
Crowd Evacuation

scholarly journals OpenGL 3D crowd evacuation simulation at universiti tun hussein onn malaysia (UTHM) hostel

2019 ◽  
Vol 16 (2) ◽  
pp. 1034
Author(s):  
Jamaludin, M.N ◽  
Mohamad, S. ◽  
Sunar, M.S. ◽  
Isa, K. ◽  
Hanifa, R.M. ◽  
...  
Keyword(s):  
Computer Application ◽  
Force Model ◽  
Social Force ◽  
Social Force Model ◽  
Evacuation Simulation ◽  
Interaction Forces ◽  
Fire Hazards ◽  
Main Research ◽  
Crowd Evacuation ◽  
High Level

<span lang="EN-GB">Crowd simulation is the process of simulating characterized agents or entities using computer application to analyse it in virtual scene or virtual environment. This paper investigates the best route path for agents to act in avoiding the fire hazards with different designated type of stairs in shop lots that were converted to hostel dormitory for students. 3D social force agent’s model and 3D fire hazards were designed in Microsoft Visual Studio C++ software and OpenGL library. A research was conducted using social force model behaviour and were taken by 10 and 15 agents to analyse the time taken to complete the evacuation process. The acceleration produced where it is related with route path taken by agents, interaction forces of agents and interaction forces of wall are the main research system to analyse agents’ behaviour during simulation. Different simulations have been used to determine the best and fastest route taken by agents. In summary, the lower the number of agents, the lower the time allocated by agents to complete the evacuation. Finally, less number of agents using the designated straight stairs gave a lower time to complete evacuation process and reached high level of security to avoid being exposed to fire hazards. </span>

scholarly journals A re-examination of the role of friction in the original Social Force Model

2020 ◽  
Vol 121 ◽  
pp. 42-53 ◽  
Author(s):  
I.M. Sticco ◽  
G.A. Frank ◽  
F.E. Cornes ◽  
C.O. Dorso
Keyword(s):  
Force Model ◽  
Social Force ◽  
Social Force Model

scholarly journals Crowd Evacuation Guidance Based on Combined Action Reinforcement Learning

Algorithms ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 26
Author(s):  
Yiran Xue ◽  
Rui Wu ◽  
Jiafeng Liu ◽  
Xianglong Tang
Keyword(s):  
Reinforcement Learning ◽  
Guidance System ◽  
Force Model ◽  
Interactive Simulation ◽  
Social Force ◽  
Novel Approach ◽  
Learning Agent ◽  
Network Output ◽  
Combined Action ◽  
Crowd Evacuation

Existing crowd evacuation guidance systems require the manual design of models and input parameters, incurring a significant workload and a potential for errors. This paper proposed an end-to-end intelligent evacuation guidance method based on deep reinforcement learning, and designed an interactive simulation environment based on the social force model. The agent could automatically learn a scene model and path planning strategy with only scene images as input, and directly output dynamic signage information. Aiming to solve the “dimension disaster” phenomenon of the deep Q network (DQN) algorithm in crowd evacuation, this paper proposed a combined action-space DQN (CA-DQN) algorithm that grouped Q network output layer nodes according to action dimensions, which significantly reduced the network complexity and improved system practicality in complex scenes. In this paper, the evacuation guidance system is defined as a reinforcement learning agent and implemented by the CA-DQN method, which provides a novel approach for the evacuation guidance problem. The experiments demonstrate that the proposed method is superior to the static guidance method, and on par with the manually designed model method.

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