Group Stratification and Differentiated Guidance Strategies in Emergency Evacuation

Problem Description
In emergency evacuation scenarios (such as fires, earthquakes, etc.), crowds are typically composed of individuals with different characteristics, for example, age (elderly, children), physical fitness (disabled, injured), cognitive ability (familiar/unfamiliar with the environment) and even roles (rescuers, ordinary people). Group stratification refers to dividing the crowd into different subgroups based on these characteristics; differentiated guidance strategies involve designing targeted evacuation plans tailored to the features of each subgroup. The core issue is: how to optimize overall evacuation efficiency through stratification while ensuring the safety of vulnerable groups.

Step-by-Step Explanation of the Solution Process

1. Analysis of Key Dimensions for Group Stratification

   • Mobility Capability: Includes walking speed, endurance, and reaction agility. For example, the average speed of young people is 1.5 m/s, while that of the elderly or disabled may only be 0.5-1.0 m/s.
   • Environmental Familiarity: Those familiar with the environment can quickly find alternative exits, while unfamiliar individuals tend to follow the mainstream crowd or signs.
   • Risk Perception Differences: Some groups may underestimate the danger (e.g., lingering to take photos), while panicked groups may overreact, leading to congestion.
   • Social Roles: Rescuers need to move against the flow, which may affect the capacity of the main evacuation routes.
     Objective of Stratification: Avoid a "one-size-fits-all" approach leading to resource misallocation, such as fast groups being blocked by slow groups.

2. Methods for Stratified Data Collection and Modeling

   • Determine the proportion of each subgroup through surveys or historical data (e.g., the elderly account for 15% of shopping mall visitors).
   • Set parameters for different subgroups in simulation models:
     • Movement Models: Use social force models to adjust speed and force magnitude.
     • Decision Models: Set path selection logic (e.g., those familiar with the environment prioritize the shortest path, while unfamiliar individuals follow the crowd).
   • Example: In AnyLogic or Pathfinder simulations, define the "Elderly" agent attributes with a minimum speed of 0.7 m/s and a reaction delay of 2 seconds.

3. Design of Differentiated Guidance Strategies

   • Spatial Stratification Guidance:
     • Reserve dedicated passages or areas near exits for slow-moving groups (e.g., setting up collection points for the disabled near elevators).
     • Dynamic exit allocation: Assign main exits to fast groups, and guide slow groups to side or backup exits to reduce cross-flow.
   • Time-Staggered Guidance:
     • Phased evacuation: First guide fast groups to clear main pathways, then notify slow groups to start, avoiding mixed congestion.
     • Delayed start strategies: Issue delayed evacuation instructions for rescuers or key personnel.
   • Differentiated Information Delivery:
     • Send concise, calming instructions to panicked groups (e.g., "Exit 50 meters to the left"), and provide detailed paths to calm groups.
     • Use color/sound coding for guidance: For example, broadcast "Green signs point to accessible routes."

4. Strategy Effectiveness Evaluation and Trade-offs

   • Establish evaluation metrics:
     • Total Evacuation Time: Whether it is shortened after stratification.
     • Fairness: Whether the survival rate of vulnerable groups improves (e.g., the proportion of delay time for the elderly).
     • System Stability: Whether abnormal behavior in a subgroup triggers chain congestion.
   • Compare scenarios through multi-agent simulation:
     • Case study: In a stadium without stratification strategies, the total evacuation time is 500 seconds, with a high casualty rate among the elderly; after implementing channel-based guidance, the total time increases to 520 seconds, but the survival rate of the elderly improves by 30%.
     • Trade-off analysis: Accept a slight increase in time to improve fairness, or optimize paths to reduce total losses.

5. Dynamic Adjustment and Real-Time Optimization

   • Use monitoring data (cameras, sensors) to detect congestion points in real-time and dynamically adjust guidance strategies:
     • If congestion is detected in areas with elderly individuals, temporarily open emergency passages and notify volunteers to assist.
     • Information updates: Send rerouting instructions to groups already in motion (e.g., "Original exit congested, please proceed to Exit B").
   • Machine learning applications: Train models to predict bottlenecks based on real-time crowd distribution and automatically switch guidance plans.

Summary
The core of group stratification and differentiated guidance is "categorical governance." By precisely matching subgroup characteristics with resource allocation, an optimal balance between efficiency and fairness is sought. In practical applications, strategies need to be iteratively optimized based on specific scenario data, and ethical considerations (such as whether to disclose stratification criteria) must be addressed.