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research-article

SPH crowds: : Agent-based crowd simulation up to extreme densities using fluid dynamics

Authors:

Wouter van Toll,

Thomas Chatagnon,

Barbara Solenthaler,

Julien PettréAuthors Info & Claims

Volume 98, Issue C

Pages 306 - 321

https://doi.org/10.1016/j.cag.2021.06.005

Published: 01 August 2021 Publication History

Highlights

•

Extreme-density crowds (4+ people per square meter) bear similarities to fluids.

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We extend agent-based crowd simulation to extreme densities using Smoothed Particle Hydrodynamics (SPH).

•

SPH forces augment the usual navigation behavior and contact forces for each agent.

•

Depending on density, agents blend between collision avoidance and fluid-like interactions.

•

SPH improves stability, density control, and replication of shockwaves, all in real-time.

Graphical abstract

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Abstract

In highly dense crowds of humans, collisions between people occur often. It is common to simulate such a crowd as one fluid-like entity (macroscopic), and not as a set of individuals (microscopic, agent-based). Agent-based simulations are preferred for lower densities because they preserve the properties of individual people. However, their collision handling is too simplistic for extreme-density crowds. Therefore, neither paradigm is ideal for all possible densities.

In this paper, we combine agent-based crowd simulation with Smoothed Particle Hydrodynamics (SPH), a particle-based method that is popular for fluid simulation. We integrate SPH into the crowd simulation loop by treating each agent as a fluid particle. The forces of SPH (for pressure and viscosity) then augment the usual navigation behavior and contact forces per agent. We extend the standard SPH model with a dynamic rest density per particle, which intuitively controls the crowd density that an agent is willing to accept. We also present a simple way to let agents blend between individual navigation and fluid-like interactions depending on the SPH density.

Experiments show that SPH improves agent-based simulation in several ways: better stability at high densities, more intuitive control over the crowd density, and easier replication of wave-propagation effects. Also, density-based blending between collision avoidance and SPH improves the simulation of mixed-density scenarios. Our implementation can simulate tens of thousands of agents in real-time. As such, this work successfully prepares the agent-based paradigm for crowd simulation at all densities.

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Cited By

Zhou YLai PYu JXiong YYang HCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Hydrodynamics-Informed Neural Network for Simulating Dense Crowd Motion PatternsProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681277(4553-4561)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681277
Ruprecht IMichelic FEggeling EPreiner R(2024)Adaptive movement behavior for real-time crowd simulationThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-024-03476-240:7(4789-4803)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1007/s00371-024-03476-2
Guy SSueda SKaramouzas IZordan V(2022)Foreword to the special section on motion, interaction, and games 2020Computers and Graphics10.1016/j.cag.2021.12.001102:C(A3)Online publication date: 1-Feb-2022
https://dl.acm.org/doi/10.1016/j.cag.2021.12.001

Index Terms

SPH crowds: Agent-based crowd simulation up to extreme densities using fluid dynamics
1. Computing methodologies
  1. Computer graphics
    1. Animation
      1. Physical simulation
    2. Shape modeling
  2. Modeling and simulation
    1. Simulation types and techniques
2. Mathematics of computing
  1. Continuous mathematics
    1. Topology

Index terms have been assigned to the content through auto-classification.

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Information

Published In

cover image Computers and Graphics

Computers and Graphics Volume 98, Issue C

Aug 2021

347 pages

ISSN:0097-8493

Issue’s Table of Contents

Elsevier Ltd.

Publisher

Pergamon Press, Inc.

United States

Publication History

Published: 01 August 2021

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Cited By

Zhou YLai PYu JXiong YYang HCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Hydrodynamics-Informed Neural Network for Simulating Dense Crowd Motion PatternsProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681277(4553-4561)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681277
Ruprecht IMichelic FEggeling EPreiner R(2024)Adaptive movement behavior for real-time crowd simulationThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-024-03476-240:7(4789-4803)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1007/s00371-024-03476-2
Guy SSueda SKaramouzas IZordan V(2022)Foreword to the special section on motion, interaction, and games 2020Computers and Graphics10.1016/j.cag.2021.12.001102:C(A3)Online publication date: 1-Feb-2022
https://dl.acm.org/doi/10.1016/j.cag.2021.12.001

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