Simulation Visualization and Result Interpretation Methods in Crowd Evacuation

Topic Description
This knowledge point explores how to transform data generated by crowd evacuation simulations (such as individual trajectories, density distributions, exit flow rates, etc.) into intuitive visualization results and establish a systematic analysis framework to interpret these results. Key aspects include criteria for selecting visualization techniques, the logic for extracting key indicators, and how to identify behavioral patterns and system bottlenecks from simulation outputs.

Solution Process

1. Define Visualization Objectives

   • Problem Identification: Evacuation simulation outputs are typically multi-dimensional time series data (e.g., the position of each individual over time). Visualization needs to address two types of questions:
     • Macroscopic Trends: Overall evacuation efficiency (e.g., total evacuation time), congestion evolution patterns.
     • Microscopic Mechanisms: The impact of individual decisions on the global outcome (e.g., path deviation caused by herd behavior).
   • Examples: If focusing on the balance of exit usage, it is necessary to visualize the temporal changes in pedestrian flow at each exit; if studying panic propagation, it requires overlaying heatmaps of emotional states and movement speeds.

2. Select Visualization Techniques

   • Comparison of Basic Methods:

     Technique Type	Applicable Scenarios	Limitations
     Trajectory Animation	Intuitive display of dynamic processes	Difficult for quantitative analysis
     Heatmap (Density)	Identifying congestion areas	Loss of individual details
     Line Chart/Bar Chart	Comparing key indicators (flow, density)	Lack of spatial information

   • Advanced Combination Strategies:
     • Use Spatial Overlay Method: Simultaneously display density heatmaps (color intensity represents the number of people) and individual trajectory arrows (direction indicates movement trend) on a floor plan.
     • Incorporate Timeline Controls: Allow interactive backtracking to the state at a specific moment, aiding in the analysis of event chains (e.g., a single individual's pause triggering chain congestion).

3. Extract Key Indicators

   • Efficiency Indicators:
     • Total Evacuation Time (from the first individual's movement to the last individual's exit).
     • Variance in Exit Utilization (reflects whether the use of each exit is balanced, calculation formula: \(\frac{1}{n}\sum_{i=1}^n (f_i - \bar{f})^2\), where \(f_i\) is the flow rate of exit \(i\)).
   • Safety Indicators:
     • High-Density Duration (cumulative time when density exceeds the threshold of 2 persons/㎡).
     • Conflict Count (number of events where the distance between individuals is less than the safe distance of 0.5m).

4. Establish an Interpretation Framework

   • Correlation Analysis:
     • For example, when a sudden drop in flow at Exit A is observed, simultaneously check if a density peak appears in the nearby area, hypothesizing that congestion caused the crowd to redirect.
   • Causal Inference:
     • Use controlled variable methods (e.g., comparing simulations with and without guidance signs) to verify whether patterns observed in visualizations are caused by specific factors.
   • Uncertainty Handling:
     • Statistical distributions from multiple simulations (e.g., box plots of evacuation times) can distinguish between random fluctuations and stable patterns.

5. Case Demonstration

   • Steps:
     1. Generate evacuation simulation data involving 500 individuals (initially randomly distributed, with two exits).
     2. Draw a temporal heatmap: X-axis represents time, Y-axis represents spatial location (sorted by distance to the exit), color indicates density.
     3. Discover red stripes (high density) appearing on the heatmap at t=120s; corresponding trajectory animation shows this location is a corridor corner.
     4. Combine indicators: High density at this location persists for 60s, causing a 25% increase in total evacuation time.
   • Conclusion: The corner is a bottleneck, requiring optimization through design widening or guided分流 (flow diversion).

Summary
Visualization is not only a tool for presenting results but also a bridge connecting simulation data and behavioral mechanisms. Through multi-dimensional indicator correlation and dynamic interactive analysis, actionable evacuation optimization strategies can be extracted from complex data.