Psychological and Behavioral Modeling in Crowd Evacuation

Psychological and Behavioral Modeling in Crowd Evacuation

Problem Description
In emergency evacuation scenarios (e.g., fire, earthquake), the psychological states (e.g., panic, herd behavior) and behavioral patterns (e.g., route selection, movement speed) of a crowd significantly impact evacuation efficiency. How can we model the influence of psychological factors on group behavior quantitatively and optimize evacuation strategies?

Problem-Solving Process

1. Problem Decomposition

  • Psychological Factors: Level of panic, herd mentality, information perception capability.
  • Behavioral Manifestations: Changes in movement speed, route deviation, formation of congestion.
  • Modeling Objective: Transform psychological states into quantifiable parameters and predict their impact on the evacuation process.

2. Key Parameter Definitions

  • Panic Index (PI):
    • Formula: \(PI = f(\text{danger distance}, \text{information ambiguity}, \text{crowd density})\)
    • Example: PI value increases when danger is closer, information is less clear, and density is higher.
  • Herd Tendency (HT):
    • Definition: The probability of an individual following others' movement, positively correlated with the decision consistency of the surrounding crowd.
  • Effective Movement Speed:
    • Base speed is affected by panic: high panic may cause acceleration (blind running) or deceleration (freezing).

3. Building the Behavioral Model
Step 1: Individual Decision-Making Model

  • Using the Perception-Decision-Action framework:
    • Perception: Individuals update their psychological state based on the surrounding environment (exit visibility, others' movement directions).
    • Decision:
      • If PI is low: Rationally choose the shortest path.
      • If PI is high: Prioritize HT value, follow the nearest crowd movement.
    • Action: Adjust movement direction and speed according to the decision.

Step 2: Group Interaction Simulation

  • Implement via Cellular Automata (CA) or Social Force Model (SFM):
    • Cellular Automata: Discretize space into grids; each grid's state (empty/occupied) is influenced by neighbors, with rules incorporating threshold judgments for PI and HT.
    • Social Force Model: Introduce psychological forces (e.g., herding force, panic driving force) to modify Newton's equations of motion.

4. Simulation and Optimization

  • Simulation Design:
    • Set up different scenarios (e.g., varying exit widths, information notification methods) to test evacuation times.
    • Record the formation and dissipation of congestion points, analyzing the amplification effect of psychological factors (e.g., panic contagion).
  • Strategy Optimization:
    • Increase guidance signage (reduce information ambiguity → lower PI).
    • Implement phased/zonal evacuation (reduce negative chain reactions from herd behavior).
    • Train emergency personnel to intervene with high-panic individuals (reset their PI values).

5. Validation and Adjustment

  • Compare with historical evacuation data or experimental videos to calibrate model parameters (e.g., weights in PI).
  • Introduce machine learning: Train the mapping relationship between psychological states and behavior using real data to improve prediction accuracy.

Summary
The core of psychological and behavioral modeling lies in transforming subjective factors (panic, herd behavior) into computable variables and revealing group dynamics through multi-agent simulation. Optimization requires combining engineering measures (e.g., spatial design) and psychological interventions (e.g., information transparency).