Behavior Prediction in Crowd Evacuation and Machine Learning Applications

Problem Description
In crowd evacuation scenarios, individual behavior is influenced by multiple factors such as physiology, psychology, and environment, exhibiting high complexity. Traditional models (e.g., social force models) rely on preset rules and struggle to accurately capture nonlinear behavioral patterns. Machine learning (ML) techniques, through data-driven approaches, can learn behavioral patterns from historical or simulation data, improving the accuracy of behavior prediction and thereby optimizing evacuation strategies. This topic explores how to use machine learning methods to predict crowd evacuation behavior, including data sources, feature engineering, model selection, and practical application challenges.

Solution Process

1. Problem Definition and Data Collection

   • Objective: Predict dynamic behaviors of individuals during evacuation (e.g., movement speed, direction choice, decision delay).
   • Data Sources:
     • Experimental Data: Evacuation experiments in controlled environments (e.g., video recordings, sensor trajectory data).
     • Simulation Data: Generate large amounts of virtual evacuation data using traditional models (e.g., social force models), adding noise to simulate real-world uncertainty.
     • Real-World Data: Surveillance videos from public spaces (requires handling privacy issues and involves high annotation costs).
   • Key Metrics: Trajectory coordinates, timestamps, individual attributes (age, gender), environmental features (exit locations, obstacle density).

2. Feature Engineering

   • Individual-Level Features:
     • Current speed, acceleration, distance to the nearest exit, spacing to surrounding individuals.
     • Historical behavior sequences (e.g., changes in movement direction over the past 5 seconds).
   • Group-Level Features:
     • Local density (based on Voronoi diagrams or kernel density estimation).
     • Group motion consistency (e.g., optical flow features or resultant force direction from social force models).
   • Environmental Features:
     • Obstacle distribution, exit visibility, location of guidance signs.
   • Processing Techniques: Normalize continuous features, use sliding windows to generate samples for spatiotemporal sequence data.

3. Model Selection and Training

   • Regression Problem (predicting movement speed):
     • Linear Models (e.g., Ridge Regression) as baselines, with strong interpretability but limited ability to capture nonlinear relationships.
     • Tree Models (e.g., Gradient Boosting Trees like XGBoost): Effective at handling feature interactions, suitable for small to medium-sized datasets.
   • Classification Problem (predicting direction choice):
     • Logistic Regression: Baseline model, outputs choice probabilities.
     • Random Forest: Reduces overfitting risk through voting from multiple trees.
   • Sequence Prediction Problem (predicting trajectories):
     • Recurrent Neural Networks (RNN/LSTM): Handle temporal dependencies, e.g., predicting future positions based on historical trajectories.
     • Graph Neural Networks (GNN): Model interactions between individuals (e.g., constructing graph structures based on distances), more suitable for group dynamics.
   • Training Key Points:
     • Split training/validation sets based on time series to avoid data leakage.
     • Loss functions should incorporate evacuation scenarios (e.g., prediction errors may lead to severe congestion, requiring weighted handling).

4. Model Evaluation and Optimization

   • Evaluation Metrics:
     • Regression tasks: Root Mean Square Error (RMSE), Mean Absolute Error (MAE).
     • Classification tasks: Accuracy, F1-score (addressing class imbalance issues).
     • Trajectory prediction: Final Displacement Error (FDE), collision rate (whether predicted trajectories overlap with others).
   • Optimization Strategies:
     • Introduce physical constraints (e.g., predicted trajectories cannot pass through walls) via post-processing or model structure embedding.
     • Ensemble learning: Combine multiple models (e.g., social force models + ML models) to enhance robustness.
     • Online learning: Update models in real-time during evacuation to adapt to dynamic changes.

5. Practical Application Challenges

   • Data Scarcity: Limited real evacuation data; mitigated by transfer learning (pre-training on simulation data, fine-tuning on real data).
   • Real-Time Requirements: Complex models (e.g., deep networks) are computationally intensive; trade-offs between accuracy and speed are needed, or edge computing can be utilized.
   • Interpretability: Black-box model decisions are hard to trace; tools like SHAP can assist in safety verification.
   • Ethical Risks: Avoid models reinforcing biases (e.g., neglecting behaviors of vulnerable groups); ensure diversity in training data.

Summary
Machine learning compensates for the limitations of traditional models through data-driven methods but must closely integrate with the characteristics of evacuation scenarios (e.g., spatiotemporal dynamics, social interactions). Future directions include combining physical rules with deep learning (physics-informed neural networks), multi-agent reinforcement learning, etc., to achieve more intelligent adaptive evacuation systems.