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Pedestrian Flow Model Based on Cellular Automata Under Visual Trajectory and Multi-Scenario Evacuation Simulation

Yueyue Chen1, Jinbao Yao1, Chenze Gao1

  • 1School of Civil Engineering, Beijing Jiaotong University, Beijing 100044, China.

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|March 14, 2026
PubMed
Summary
This summary is machine-generated.

This study integrates computer vision and cellular automata (CA) for pedestrian flow simulation. The new method enhances safety design by modeling crowd behavior, including panic and group dynamics, for better emergency management.

Keywords:
YOLO-DeepSORTcellular automataevacuation simulationpanic behaviorpedestrian flow analysis

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Area of Science:

  • Computational Social Science
  • Computer Vision
  • Traffic Engineering

Background:

  • Accurate pedestrian flow modeling is vital for public safety and emergency planning.
  • Existing models often lack real-world data integration for nuanced crowd behavior.

Purpose of the Study:

  • To develop an interdisciplinary method combining computer vision and cellular automata (CA) for precise pedestrian flow simulation.
  • To enhance evacuation models with data-driven transition probabilities and behavioral factors.

Main Methods:

  • Collected and analyzed unidirectional pedestrian flow video data using YOLOv11 and DeepSORT for high-precision trajectory tracking.
  • Converted trajectory data into data-driven transition probabilities for the CA model's Moore neighborhood.
  • Developed an improved evacuation model incorporating real-data transition probabilities, speed-density, panic, life value, and hazard dynamics.

Main Results:

  • Simulations revealed moderate panic can shorten evacuation times, but excessive panic leads to disordered behavior.
  • Group movement is limited by the slowest individual, and faster hazard sources decrease the number of safe pedestrians.
  • The model provides insights into crowd dynamics under various emergency scenarios.

Conclusions:

  • The integrated computer vision and CA approach offers a novel method for refined pedestrian evacuation simulation.
  • Findings support improved safety design and emergency management strategies in public spaces.
  • Data-driven transition probabilities significantly enhance the realism of CA-based evacuation models.