physics-based-pedestrian-modeling 0.2.0

Python package to create physics-based pedestrian models from crowd measurements

Data-driven physics-based modeling of pedestrian dynamics

Project Overview

Python package to create physics-based pedestrian models from pedestrian trajectory measurements. This package is an implementation of the data-driven generalized pedestrian model presented in:

Pouw, C. A. S., van der Vleuten, G., Corbetta, A., & Toschi, F. (2024). Data-driven physics-based modeling of pedestrian dynamics. Preprint, https://arxiv.org/abs/2407.20794

Documentation

• Documentation: https://c-pouw.github.io/physics-based-pedestrian-modeling.

Usage Notebooks

We provide the following usage notebook on Google Colab:

^{_{- Generalized pedestrian model.}}

The notebook can be used to create a model for the following environments:

• Walking paths in a narrow corridor
• Intersecting walking paths
• Walking paths on a train station platform

Getting started

Install the package from source

git clone https://github.com/c-pouw/physics-based-pedestrian-modeling.git
cd physics-based-pedestrian-modeling
pip install -e .

Run the main processing script for one of the available parameter files (listed below)

python physped/main.py params=PARAM_NAME

Parameter Files

Configuration of parameter files is handled by . Default parameter files are provided for the following cases:

• single_paths: Trajectories in a narrow corridor.
• parallel_paths: Trajectories in a wide corridor.
• curved_paths_synthetic: Trajectories along a closed elliptical path.
• intersecting_paths: Trajectories intersecting in the origin.
• station_paths: Complex trajectories in a train station.

Features

Preprocessing of trajectories

Calculate slow dynamics

Learn potential from the preprocessed trajectories

Learn the potential

Simulate new trajectories using the learned potential

Simulate new trajectories

License

• Free software: 3-clause BSD license