A Novel Cellular Automaton Model for Optimized Multi-Exit Crowd Evacuation in High-Density Environments

Evacuation systems play a critical role in mitigating casualties and property damage during emergency situations. Comprehending evacuee behavior in overcrowded scenarios is vital for developing effective evacuation strategies. Nonetheless, evacuating large crowds from buildings with multiple exits presents a significant challenge, particularly when exits are asymmetrical and crowds are dense. This paper proposes an innovative two-dimensional cellular automaton model for multi-exit evacuation, designed to streamline evacuee decision-making in an asymmetrical exit layout within dense crowds. The proposed model addresses limitations in traditional evacuation models by incorporating dynamic counting areas and enhanced parameters, including congestion, distance, and empty parameters. These improvements enable the model to more accurately represent evacuee behavior during emergency situations, particularly in environments with multiple exits. The model effectively captures complex crowd dynamics, such as arching and shockwaves, and adjusts exit selection based on real-time congestion levels. Simulations demonstrate that the proposed model significantly reduces evacuation time, unit evacuation time, and travel distance compared to existing models. Key findings highlight the model's ability to mitigate bottlenecks at congested exits, optimize exit utilization, and enhance overall evacuation efficiency. Future research aims to refine the model with real-world data for broader applications in diverse settings like stadiums and airports, thereby improving safety protocols during emergencies.