Evolutionary Game of Emergency Evacuation After an Earthquake at a University: How to Promote Orderly Evacuation

Guo, Yong‐Xin; Song, Yan; Chen, Weiling

doi:10.1109/access.2020.3046618

Cited by 2 publications

(3 citation statements)

References 36 publications

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“…If one player chooses a positive strategy and the other chooses a negative strategy, the player who chooses to betray receives a temptation payoff ( ), while the player who chooses to cooperate receives a neutral payoff ( ). Furthermore, in the literature 52 , it has been proven that in order for the assumptions to be generalized, the following condition must be met: . In summary, the gains or losses are represented by setting parameters, which include the players’ subjective psychological evaluations and neutral constraint conditions.…”

Section: Methodsmentioning

confidence: 99%

“…However, the subjective psychological states of participants are difficult to quantify directly with numbers. Through the methods presented in literature such as 52 , 53 , indirect parameters and the relationships between parameters can be used to represent the effects of subjective psychological states. For example, the psychological state of participants can be influenced by the cost-benefit relationship, which can more accurately explain how psychological states guide behavior.…”

Section: Methodsmentioning

confidence: 99%

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Optimization of construction safety resource allocation based on evolutionary game and genetic algorithm

Peng,

Zhang,

Feng

et al. 2023

Sci Rep

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In the construction industry, ensuring the safety performance of a project relies heavily on the effective allocation of safety resources. As the importance of mental health in the construction industry increases, evolutionary game theory can be used to analyze the interaction mechanism of various factors affecting safety performance during the construction phase. The objective of this paper is to construct an analytical model that combines evolutionary game theory with genetic algorithms from the perspective of Leader-Member Exchange Ambivalence. The model aims to quantify and compare the various factors that influence achieving the expected safety state and identify the specific necessary constraints. Initially, we analyzed the relationships among construction site employees, divided them into superiors and subordinates, and established a game model and payoff matrix based on the research background. Next, we introduced genetic algorithms into the model via the replicator dynamic equation for optimization. We adjusted the coefficients of safety risk level, psychological expected return, moral identity, and other factors to simulate various construction site scenarios. Simulation and optimization results indicate that genetic algorithms provide more accurate reference values for safety resource allocation compared to preset or manually assigned values.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Optimization of construction safety resource allocation based on evolutionary game and genetic algorithm

Peng,

Zhang,

Feng

et al. 2023

Sci Rep

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“…Crowd evacuation is a complex and challenging problem, particularly in scenarios involving dynamically moving hazards [1]- [5]. The ability to navigate safely and efficiently while avoiding obstacles is critical for successful evacuation [1], [2], [6]- [9]. This paper proposes a novel application of the emotional reciprocal velocity obstacles (ERVO) method for obstacle avoidance in dynamic hazard scenarios.…”

Section: Introductionmentioning

confidence: 99%

Crowd navigation for dynamic hazard avoidance in evacuation using emotional reciprocal velocity obstacles

Fachri,

Prasetyo,

Damastuti

et al. 2024

IJ-AI

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Crowd evacuation can be a challenging task, especially in emergency situations involving dynamically moving hazards. Effective obstacle avoidance is crucial for successful crowd evacuation, particularly in scenarios involving dynamic hazards such as natural or man-made disasters. In this paper, we propose a novel application of the emotional reciprocal velocity obstacles (ERVO) method for obstacle avoidance in dynamic hazard scenarios. ERVO is an established method that incorporates agent emotions and obstacle avoidance to produce more efficient and effective crowd navigation. Our approach improves on previous research by using ERVO to model the perceptive danger posed by dynamic hazards in real-time, which is crucial for rapid response in emergency situations. We conducted experiments to evaluate our approach and compared our results with other velocity obstacle methods. Our findings demonstrate that our approach is able to improve agent coordination, reduce congestion, and produce superior avoidance behavior. Our study shows that incorporating emotional reciprocity into obstacle avoidance can enhance crowd behavior in dynamic hazard scenarios.

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Evolutionary Game of Emergency Evacuation After an Earthquake at a University: How to Promote Orderly Evacuation

Cited by 2 publications

References 36 publications

Optimization of construction safety resource allocation based on evolutionary game and genetic algorithm

Optimization of construction safety resource allocation based on evolutionary game and genetic algorithm

Crowd navigation for dynamic hazard avoidance in evacuation using emotional reciprocal velocity obstacles

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