# Data-Driven Crowd Simulation with Generative Adversarial Networks

arXiv: https://arxiv.org/abs/1905.09661 · HTML: https://mlsanity.com/paper/1905.09661/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 May 2019.

## Built on by (most cited first, 4 of 4)

- [Deep Learning for Cross-Domain Data Fusion in Urban Computing: Taxonomy, Advances, and Outlook](https://mlsanity.com/paper/2402.19348/) (2024)
- [Safety-compliant Generative Adversarial Networks for Human Trajectory Forecasting](https://mlsanity.com/paper/2209.12243/) (2022)
- [A Perceptually-Validated Metric for Crowd Trajectory Quality Evaluation](https://mlsanity.com/paper/2108.12346/) (2021)
- [ODEs learn to walk: ODE-Net based data-driven modeling for crowd dynamics](https://mlsanity.com/paper/2210.09602/) (2022)

## Abstract (arXiv)

This paper presents a novel data-driven crowd simulation method that can mimic the observed traffic of pedestrians in a given environment. Given a set of observed trajectories, we use a recent form of neural networks, Generative Adversarial Networks (GANs), to learn the properties of this set and generate new trajectories with similar properties. We define a way for simulated pedestrians (agents) to follow such a trajectory while handling local collision avoidance. As such, the system can generate a crowd that behaves similarly to observations, while still enabling real-time interactions between agents. Via experiments with real-world data, we show that our simulated trajectories preserve the statistical properties of their input. Our method simulates crowds in real time that resemble existing crowds, while also allowing insertion of extra agents, combination with other simulation methods, and user interaction.