Analysis of Environmental Factors and Spatial Layout Influences in Crowd Evacuation

Problem Description
The analysis of environmental factors and spatial layout influences is a key issue in crowd evacuation research. It focuses on how the physical structure of buildings/spaces (such as corridor width, turning angles, staircase design) and environmental conditions (such as visibility, temperature, toxic smoke) affect the flow efficiency, safety, and behavioral decisions of crowds. For example, narrow passages may cause bottleneck effects, while smoke-filled environments can significantly reduce movement speed and trigger panic. The core objective is to quantify the impact of environmental parameters on the evacuation process to provide a basis for spatial design or real-time intervention.

Problem-Solving Process

1. Identify Key Environmental Parameters

   • Spatial Geometric Features: Include exit width, corridor length and width, staircase slope, turning radius, etc. For example, exit width must satisfy the "flow capacity" formula (flow = width × flow velocity × personnel density).
   • Environmental Dynamic Factors: Such as smoke concentration in fires (affecting visibility and respiration), temperature (causing burns or physical decline), and structural collapse risk. These require real-time monitoring via sensor data or physical models (e.g., fire spread models).
   • Obstacle Layout: Fixed obstacles (e.g., columns) or temporary obstacles (e.g., debris) can alter path choices, potentially creating "dead zones" or diversion points.

2. Establish Association Models Between Parameters and Evacuation Indicators

   • Flow Velocity Model: Based on "speed-density" relationships (e.g., the Fruin model or Weidmann fundamental diagram), considering environmental interference. For example:
     • When visibility is below 5 meters, walking speed may decrease to 30% of the normal value;
     • In high-temperature environments (>60°C), personnel mobility declines sharply.
   • Bottleneck Effect Analysis: Evaluate passage efficiency of channels with different widths using "Level of Service" (LOS). For example:
     • A 1.2-meter-wide channel only supports unidirectional flow, while a 2.4-meter channel can support bidirectional flow but requires management of conflict points.
   • Behavioral Correction Factors: Environmental stress may lead to irrational behaviors (e.g., turning back, pushing), requiring the inclusion of panic coefficients in the model (e.g., "following behavior increases when field of view is limited").

3. Simulation and Data Analysis

   • Use agent-based simulation (e.g., AnyLogic or NetLogo) to inject environmental parameters:
     1. Set up a spatial grid, labeling attributes of different areas (e.g., "high-smoke zone," "narrow passage");
     2. Assign environmental response rules to agents (e.g., "automatically slow down and seek handrails when visibility < 10 meters");
     3. Run multi-scenario tests (e.g., comparing evacuation times for "wide corridor + smoke" vs. "narrow corridor + no smoke").
   • Output key indicators: total evacuation time, congestion point distribution, casualty probability (e.g., duration threshold of exposure to toxic environments).

4. Optimization and Intervention Strategies

   • Spatial Design Optimization: Adjust layout based on simulation results. For example:
     • Increase width at bottlenecks or set up buffer zones to reduce pedestrian conflicts;
     • Use curved designs at turns to avoid "corner congestion."
   • Dynamic Intervention: Adjust guidance strategies in real-time based on environmental monitoring data. For example:
     • When temperature soars in a certain area, use emergency broadcasts to instruct people to detour;
     • Utilize ventilation systems to control smoke diffusion paths, buying time for evacuation.

Summary
This problem requires integrating engineering (spatial design), environmental science (hazard simulation), and behavioral psychology (stress response). By modeling, qualitative environmental factors are transformed into quantifiable evacuation risk parameters, ultimately achieving the coordinated optimization of the "space-environment-crowd" system.