Modeling and Simulation of Crowd Pre-Evacuation Decision-Making in Complex Traffic Environments

Area of Science:

• Traffic simulation
• Human behavior modeling
• Urban safety

Background:

• Existing models simulate human movement in traffic but often neglect pre-evacuation decision-making.
• Improving crowd safety and urban resilience requires understanding initial human reactions in complex environments.

Purpose of the Study:

• To propose a novel pedestrian pre-evacuation decision-making model for complex environments.
• To incorporate pedestrian heterogeneity, including cognition, information, experience, habits, stress, and decision-making ability.
• To predict escape reaction time and opportunities.

Main Methods:

• Developed a decision-making model with 'stay' and 'escape' options based on individual preferences.
• Integrated factors like risk tolerance, proximity to danger, and crowd reactions.
• Constructed a regression equation to predict escape opportunities using multiple influencing factors.

Main Results:

• Escape opportunity choices are staged and influenced by individual risk tolerance, risk severity, distance to danger, and crowd behavior.
• Factors like Generation (Gen), Group, Time, and Mode positively correlate with risk tolerance, while Age and Zone show negative correlations.
• 19.81% of individuals exhibited immediate evacuation behavior in response to different strategies.

Conclusions:

• The model provides a framework for understanding and predicting pedestrian behavior in complex traffic scenarios.
• Findings can inform strategies to enhance public safety and urban resilience.
• Acknowledging individual heterogeneity is key to accurate crowd behavior prediction.