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Related Experiment Video

Updated: May 29, 2025

Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior
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Data-driven physics-based modeling of pedestrian dynamics.

Caspar A S Pouw1,2, Geert G M van der Vleuten1, Alessandro Corbetta1,3

  • 1Eindhoven University of Technology, Department of Applied Physics and Science Education, 5600 MB Eindhoven, The Netherlands.

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Summary

This study introduces a new two-timescale Langevin dynamics model to accurately simulate pedestrian movement in complex environments. The model learns effective potentials from real-world data, capturing individual pedestrian dynamics in both sparse and dense crowds.

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Area of Science:

  • Physics
  • Complex Systems
  • Data Science

Background:

  • Previous models using Langevin equations effectively captured simple pedestrian dynamics.
  • Modeling complex pedestrian movement with multiple routes and destinations remained a challenge.
  • Existing methods struggled with the interplay of intentional movement, variability, and environmental interactions.

Purpose of the Study:

  • To develop a novel, generic framework for describing pedestrian dynamics in any geometric setting.
  • To extend previous work by incorporating two timescales for more realistic movement simulation.
  • To create a data-driven model capable of learning complex potentials from real-world pedestrian trajectories.

Main Methods:

  • Developed a Langevin dynamics model with fast (stochastic fluctuations) and slow (planned path) timescales.
  • Employed a data-driven approach inspired by statistical field theories to learn potentials from trajectory data.
  • Validated the model using a high-statistics database of real-life pedestrian trajectories across diverse settings.

Main Results:

  • The model successfully captures fluctuation statistics in individual pedestrian dynamics.
  • It accurately simulates pedestrian behavior in both dilute and dense crowd conditions.
  • The framework demonstrates effectiveness across five complementary settings of increasing complexity, including a train platform.

Conclusions:

  • The novel two-timescale Langevin model provides a generic and physics-based approach to pedestrian dynamics.
  • The data-driven method allows for learning effective potentials, enhancing predictive capabilities.
  • This framework offers fundamental insights and has potential applications in traffic, crowd management, and collective animal behavior.