Simulation of collision avoidance behavior in crowd movement by data-driven approach

📰 ArXiv cs.AI

Learn to simulate collision avoidance in crowd movement using a data-driven approach, improving pedestrian safety and facility layout optimization

advanced Published 1 Jun 2026

Action Steps

Collect crowd movement data to train a data-driven model
Incorporate the pedestrian collision mechanism into the loss function to reduce collision rates
Train the model using the collected data and optimized loss function
Evaluate the model's performance using Euclidean metrics and collision rates
Apply the model to simulate crowd movement and optimize facility layouts

Who Needs to Know This

Data scientists and researchers working on crowd simulation and pedestrian safety can benefit from this approach to improve their models' accuracy and reduce collision rates

Key Insight

💡 Incorporating the pedestrian collision mechanism into the loss function can significantly reduce collision rates in crowd simulation models

Key Takeaways

Learn to simulate collision avoidance in crowd movement using a data-driven approach, improving pedestrian safety and facility layout optimization

Full Article

Title: Simulation of collision avoidance behavior in crowd movement by data-driven approach

Abstract:
arXiv:2605.31210v1 Announce Type: cross Abstract: Crowd movement simulation is essential for pedestrian safety management and facility layout optimization. Data-driven models enhance trajectory prediction accuracy under Euclidean metrics, yet they suffer from excessively high collision rates, especially in bidirectional and multidirectional flows. In this paper, we establish a novel data-driven crowd simulation model that incorporates the pedestrian collision mechanism into the loss function to

Read full paper → ← Back to Reads