# Pedestrian Simulation: A Review

arXiv: https://arxiv.org/abs/2102.03289 · HTML: https://mlsanity.com/paper/2102.03289/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 52 references that are in the map.
- First posted on arXiv 5 Feb 2021.

## Builds on (most cited first, 16 of 16)

- [Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks](https://mlsanity.com/paper/1803.10892/) (2018)
- [Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Prediction](https://mlsanity.com/paper/2002.11927/) (2020)
- [DESIRE: Distant Future Prediction in Dynamic Scenes with Interacting Agents](https://mlsanity.com/paper/1704.04394/) (2017)
- [CoverNet: Multimodal Behavior Prediction using Trajectory Sets](https://mlsanity.com/paper/1911.10298/) (2019)
- [It Is Not the Journey but the Destination: Endpoint Conditioned Trajectory Prediction](https://mlsanity.com/paper/2004.02025/) (2020)
- [Social Attention: Modeling Attention in Human Crowds](https://mlsanity.com/paper/1710.04689/) (2017)
- [Rules of the Road: Predicting Driving Behavior with a Convolutional Model of Semantic Interactions](https://mlsanity.com/paper/1906.08945/) (2019)
- [Peeking into the Future: Predicting Future Person Activities and Locations in Videos](https://mlsanity.com/paper/1902.03748/) (2019)
- [Autonomous Vehicles that Interact with Pedestrians: A Survey of Theory and Practice](https://mlsanity.com/paper/1805.11773/) (2018)
- [Recursive Social Behavior Graph for Trajectory Prediction](https://mlsanity.com/paper/2004.10402/) (2020)
- [Pedestrian Action Anticipation using Contextual Feature Fusion in Stacked RNNs](https://mlsanity.com/paper/2005.06582/) (2020)
- [Agreeing to Cross: How Drivers and Pedestrians Communicate](https://mlsanity.com/paper/1702.03555/) (2017)
- [Multi-Modal Hybrid Architecture for Pedestrian Action Prediction](https://mlsanity.com/paper/2012.00514/) (2020)
- [STINet: Spatio-Temporal-Interactive Network for Pedestrian Detection and Trajectory Prediction](https://mlsanity.com/paper/2005.04255/) (2020)
- [Deep Learning for Vision-based Prediction: A Survey](https://mlsanity.com/paper/2007.00095/) (2020)
- [Graph-SIM: A Graph-based Spatiotemporal Interaction Modelling for Pedestrian Action Prediction](https://mlsanity.com/paper/2012.02148/) (2020)

## Built on by (most cited first, 5 of 5)

- [LOKI: Long Term and Key Intentions for Trajectory Prediction](https://mlsanity.com/paper/2108.08236/) (2021)
- [Social Physics Informed Diffusion Model for Crowd Simulation](https://mlsanity.com/paper/2402.06680/) (2024)
- [A Data-driven Crowd Simulation Framework Integrating Physics-informed Machine Learning with Navigation Potential Fields](https://mlsanity.com/paper/2410.16132/) (2024)
- [STDDN: A Physics-Guided Deep Learning Framework for Crowd Simulation](https://mlsanity.com/paper/2604.02756/) (2026)
- [PedNStream: Scalable Network Flow Simulation for Pedestrian Traffic Management](https://mlsanity.com/paper/2607.01021/) (2026)

## Abstract (arXiv)

This article focuses on different aspects of pedestrian (crowd) modeling and simulation. The review includes: various modeling criteria, such as granularity, techniques, and factors involved in modeling pedestrian behavior, and different pedestrian simulation methods with a more detailed look at two approaches for simulating pedestrian behavior in traffic scenes. At the end, benefits and drawbacks of different simulation techniques are discussed and recommendations are made for future research.