Multimodal Data Fusion and Real-time Decision Support in Crowd Evacuation

Multimodal Data Fusion and Real-time Decision Support in Crowd Evacuation

Problem Description
Multimodal data fusion refers to integrating heterogeneous data from different sources (such as surveillance cameras, WiFi/Bluetooth signals, social media, sensor networks, etc.) during crowd evacuation. Through analysis and processing, it generates a comprehensive perception of the evacuation situation and supports real-time decision-making (e.g., dynamic path adjustment, resource scheduling). Core issues include: how to address the heterogeneity and noise of multi-source data, how to achieve efficient data fusion and semantic parsing, and how to build real-time decision models based on the fusion results.

Problem-Solving Process

  1. Multimodal Data Collection and Preprocessing

    • Data Source Classification:
      • Visual Data: Crowd density, movement speed, and flow direction captured by cameras.
      • Wireless Signal Data: Regional population count and dwell time statistics from mobile signaling or WiFi probes.
      • Environmental Sensor Data: Abnormal indicators such as temperature and smoke concentration.
      • Text Data: Descriptions of emergency events or help requests posted on social media.
    • Preprocessing Steps:
      • Apply noise reduction and perspective distortion correction to visual data, extracting optical flow features of crowd movement.
      • Deduplicate wireless signal data (multiple records from the same device) and map it to a physical spatial grid.
      • Use natural language processing (e.g., sentiment analysis, keyword extraction) on text data to identify urgency levels.

  2. Data Fusion Model Construction

    • Spatiotemporal Alignment: Unify data from different sources to the same timestamp and spatial coordinate system (e.g., GIS grid).
      • Example: Overlay camera-covered areas with WiFi signal hotspot areas, achieving location matching through coordinate transformation.
    • Feature-Level Fusion:
      • Use deep learning models (e.g., multi-channel convolutional neural networks) to extract feature vectors from each modality's data, concatenate them, and input them into a classifier to determine regional congestion levels.
      • Example: Camera optical flow features (velocity vectors) + WiFi signal density features → fused features → Support Vector Machine classifies congestion status.
    • Decision-Level Fusion:
      • Perform independent analysis on each modality's data and then vote on decisions. For instance, when cameras, sensors, and social media all signal "high risk," the area is comprehensively judged as requiring intervention.

  3. Real-time Decision Support System Design

    • Dynamic Path Planning:
      • Predict congestion propagation trends based on fused data and use reinforcement learning models (e.g., Q-learning) to dynamically adjust exit allocation strategies.
      • Example: When congestion is detected at Exit A while Exit B is idle, guide crowd diversion through electronic signs.
    • Resource Scheduling Optimization:
      • Combine crowd distribution and emergency event locations to allocate rescue resources (e.g., ambulances, firefighting equipment) using integer programming models.
      • Objective function: Minimize total response time, subject to constraints including resource capacity and road accessibility.

  4. Validation and Iteration

    • Use simulation platforms (e.g., AnyLogic) to simulate multimodal data input, comparing decision accuracy between single data sources and fusion strategies.
    • Implement a feedback mechanism in practical applications: Adjust weight parameters of the fusion model based on evacuation outcomes (e.g., actual evacuation time).

Key Points Summary

  • The core of multimodal data fusion lies in resolving spatiotemporal consistency and semantic complementarity.
  • Real-time decision-making requires balancing computational efficiency and accuracy, often adopting hierarchical processing (preliminary computing at edge devices + deep fusion in the cloud).
  • System reliability depends on redundant data design (allowing partial operation even if one data source fails).