# ODEs learn to walk: ODE-Net based data-driven modeling for crowd dynamics

arXiv: https://arxiv.org/abs/2210.09602 · HTML: https://mlsanity.com/paper/2210.09602/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 23 references that are in the map.
- First posted on arXiv 18 Oct 2022.

## Builds on (most cited first, 4 of 4)

- [Symplectic ODE-Net: Learning Hamiltonian Dynamics with Control](https://mlsanity.com/paper/1909.12077/) (2019)
- [Learning Continuous System Dynamics from Irregularly-Sampled Partial Observations](https://mlsanity.com/paper/2011.03880/) (2020)
- [Generating Reliable and Efficient Predictions of Human Motion: A Promising Encounter between Physics and Neural Networks](https://mlsanity.com/paper/2006.08429/) (2020)
- [Data-Driven Crowd Simulation with Generative Adversarial Networks](https://mlsanity.com/paper/1905.09661/) (2019)

## Abstract (arXiv)

Predicting the behaviors of pedestrian crowds is of critical importance for a variety of real-world problems. Data driven modeling, which aims to learn the mathematical models from observed data, is a promising tool to construct models that can make accurate predictions of such systems. In this work, we present a data-driven modeling approach based on the ODE-Net framework, for constructing continuous-time models of crowd dynamics. We discuss some challenging issues in applying the ODE-Net method to such problems, which are primarily associated with the dimensionality of the underlying crowd system, and we propose to address these issues by incorporating the social-force concept in the ODE-Net framework. Finally application examples are provided to demonstrate the performance of the proposed method.