Evacuation simulation with consideration of obstacle removal and using game theory

Lin, Guan; Wong, Sai‐Keung

doi:10.1103/physreve.97.062303

Cited by 23 publications

(10 citation statements)

References 28 publications

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“…In the future, we would like to explore efficient ways to perform obstacle removal in crowd evacuation so that evacuees can escape from blocked regions. 27 The current method uses one policy for one skill. We would like to train one single policy for all the skills by incorporating one-hot vector representation.…”

Section: Discussionmentioning

confidence: 99%

Generation of multiagent animation for object transportation using deep reinforcement learning and blend‐trees

Chen

Liu

Wong

2021

Computer Animation & Virtual

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This paper proposes a framework that integrates reinforcement learning and blend-trees to generate animation of multiple agents for object transportation. The main idea is that in the learning stage, policies are learned to control agents to perform specific skills, including navigation, pushing, and orientation adjustment. The policies determine the blending parameters of the blend-trees to achieve locomotion control of the agents. In the simulation stage, the policies are combined to control the agents to navigate, push objects, and adjust orientation of the objects. We demonstrated several examples to show that the framework is capable of generating animation of multiple agents in different scenarios.

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Section: Discussionmentioning

confidence: 99%

Generation of multiagent animation for object transportation using deep reinforcement learning and blend‐trees

Chen

Liu

Wong

2021

Computer Animation & Virtual

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“…33 Related research of evacuation modeling and simulation which implements different aspects of game theory such as a spatial game in describing risky agents is proposed by Pärnänen, 10 game theory cost function modeled using distance to the exit and number of agents is proposed by Tian et al, 33 game theory for room evacuation via lattice gas scheme can be found in Bouzat and Kuperman’s study, 34 game theory via a CA environment to study patient and impatient agents are presented by Heliövaara et al, 35 von Schantz and Ehtamo, 36 while game theory to study the effect of obstacle removal during evacuation is presented by Lin and Wong. 37…”

Section: Related Workmentioning

confidence: 99%

The role of crowd behavior and cooperation strategies during evacuation

et al. 2022

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Crowd dynamics have constituted a hotspot of research in recent times, particularly in areas where developmental progress has taken place in crowd evacuation for ensuring human safety. In high-density crowd events which happen frequently, panic or an emergency can lead to an increase in congestion which may cause disastrous incidents. Crowd control planning via simulation of people’s movement and behavior can promote safe departures from a space, despite threatening circumstances. Up until now, the evolution of distinctive types of crowd behavior towards cooperative flow remains unexplored. Hence, in this paper, we investigate the impact of potential crowd behavior, namely best-response, risk-seeking, risk-averse, and risk-neutral agents in achieving cooperation during evacuation and its connection with evacuation time using a game-theoretic evacuation simulation model. We analyze the crowd evacuation of a rectangular room with either a single-door or multiple exits in a continuous space. Simulation results show that mutual cooperation during evacuation can be realized when the agents’ population is dominated by risk-averse agents. The results also demonstrate that the risk-seeking agents tend toward aggressiveness by opting for a defector strategy regardless of the local crowd densities, while other crowd behavior shows cooperation under high local crowd density.

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“…Many scholars carried out research on pedestrian evacuation path selection. Lin and Wong proposed an evacuation simulation scheme which integrates a CA model with game theory to simulate crowd evacuation from a room with consideration of obstacle removal ( 22 ). The scheme enables pedestrians to effectively avoid obstacles but does not consider the influence of the size and shape of the obstacle on the evacuation efficiency of the algorithm.…”

Section: Related Workmentioning

confidence: 99%

Crowd Evacuation Simulation Research Based on Improved Reciprocal Velocity Obstacles (RVO) Model with Path Planning and Emotion Contagion

Jun

Zhang

2021

Transportation Research Record: Journal of the Transportation R

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Crowd evacuation simulation is an important research topic for designing reasonable building layout and effective evacuation routes. The reciprocal velocity obstacles (RVO) model is a pedestrian motion model which is used, but it does not work when complex and multiple obstacles are present in the scene. This paper proposes an improved RVO model with path planning and emotion contagion for crowd evacuation simulation. The model uses the vertices of the obstacles to construct pedestrian path nodes for planning pedestrian evacuation paths. To make the pedestrian evacuation paths simulation results more reasonable, the safety and congestion of the path nodes are considered, to plan the shortest evacuation path. Finally, a contagious disease model is introduced to study the impact of emotion contagion on the evacuation process. A crowd evacuation simulation system is developed, and simulations have been carried out in a variety of scenarios. Experiments show that the model can effectively simulate crowd evacuation, providing a powerful reference for building and layout design.

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Evacuation simulation with consideration of obstacle removal and using game theory

Cited by 23 publications

References 28 publications

Generation of multiagent animation for object transportation using deep reinforcement learning and blend‐trees

Generation of multiagent animation for object transportation using deep reinforcement learning and blend‐trees

The role of crowd behavior and cooperation strategies during evacuation

Crowd Evacuation Simulation Research Based on Improved Reciprocal Velocity Obstacles (RVO) Model with Path Planning and Emotion Contagion

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