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Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior
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A novel data-driven multi-agent pedestrian flow risk assessment framework for avoiding stampede incident.

Zi-Xuan Zhou1, Kai Liu2, Pei-Yang Wu3

  • 1College of Engineering, Peking University, Beijing, China.

Accident; Analysis and Prevention
|August 30, 2025
PubMed
Summary

This study introduces a data-driven framework to monitor high-density crowds, enhancing safety in public spaces. It enables early detection of overcrowding risks by analyzing pedestrian flow and building capacity.

Keywords:
Multi-directional pedestrian flowReinforcement learningSafety capacityStampede preventionVisual information

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Area of Science:

  • * Computational Social Science
  • * Urban Planning and Safety
  • * Artificial Intelligence in Crowd Management

Background:

  • * Existing crowd simulation tools struggle with real-world complexities like varied destinations and multi-directional flows.
  • * Monitoring high-density crowds in public spaces is crucial for preventing accidents and ensuring safety.
  • * Communal areas often feature complex spatial designs that challenge current monitoring methods.

Purpose of the Study:

  • * To introduce a novel data-driven, multi-agent framework for assessing crowd dynamics and early warning conditions.
  • * To develop a system capable of handling diverse spatial layouts and pedestrian behaviors.
  • * To enhance the prediction and prevention of overcrowding incidents in public spaces.

Main Methods:

  • * Utilizes real-time visual information and reinforcement learning for intelligent decision-making.
  • * Employs a self-iterative algorithm for trajectory planning that mimics real-world pedestrian movement.
  • * Ensures model adaptability across various scenarios without requiring parameter fine-tuning.

Main Results:

  • * The framework accurately reproduces pedestrian flow in diverse, complex scenarios.
  • * Identified a discontinuous state transition in pedestrian flow as density increases.
  • * Proposed a method to detect building traffic capacity and establish crowding warning thresholds.

Conclusions:

  • * The developed framework effectively assesses crowd dynamics and predicts early warning conditions.
  • * Rational spatial layout and information guidance can significantly improve mobility and reduce stampede risks.
  • * The approach offers a method for proactive crowd safety management without architectural expansion.