research-article

Crowd-driven mid-scale layout design

Authors:

Kun ZhouAuthors Info & Claims

ACM Transactions on Graphics (TOG), Volume 35, Issue 4

Article No.: 132, Pages 1 - 14

https://doi.org/10.1145/2897824.2925894

Published: 11 July 2016 Publication History

Abstract

We propose a novel approach for designing mid-scale layouts by optimizing with respect to human crowd properties. Given an input layout domain such as the boundary of a shopping mall, our approach synthesizes the paths and sites by optimizing three metrics that measure crowd flow properties: mobility, accessibility, and coziness. While these metrics are straightforward to evaluate by a full agent-based crowd simulation, optimizing a layout usually requires hundreds of evaluations, which would require a long time to compute even using the latest crowd simulation techniques. To overcome this challenge, we propose a novel data-driven approach where nonlinear regressors are trained to capture the relationship between the agent-based metrics, and the geometrical and topological features of a layout. We demonstrate that by using the trained regressors, our approach can synthesize crowd-aware layouts and improve existing layouts with better crowd flow properties.

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Cited By

Wang YWu WFei YZheng L(2025)CVTLayout: Automated generation of mid-scale commercial space layout via Centroidal Voronoi TessellationComputers & Graphics10.1016/j.cag.2025.104175127(104175)Online publication date: Apr-2025
https://doi.org/10.1016/j.cag.2025.104175
Lu ZLi YWang F(2025)Complex layout generation for large-scale floor plans via deep edge-aware GNNsApplied Intelligence10.1007/s10489-025-06311-w55:6Online publication date: 1-Apr-2025
https://dl.acm.org/doi/10.1007/s10489-025-06311-w
Makinoshima FTakahashi TOishi YDastani MSichman JAlechina NDignum V(2024)Bayesian Behavioural Model Estimation for Live Crowd SimulationProceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems10.5555/3635637.3662994(1355-1362)Online publication date: 6-May-2024
https://dl.acm.org/doi/10.5555/3635637.3662994
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Index Terms

Crowd-driven mid-scale layout design
1. Computing methodologies
  1. Modeling and simulation
2. Theory of computation
  1. Randomness, geometry and discrete structures
    1. Computational geometry

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cover image ACM Transactions on Graphics

ACM Transactions on Graphics Volume 35, Issue 4

July 2016

1396 pages

ISSN:0730-0301

EISSN:1557-7368

DOI:10.1145/2897824

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Copyright © 2016 ACM.

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Publication History

Published: 11 July 2016

Published in TOG Volume 35, Issue 4

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SUTD-MIT International Design Center Grant
National Natural Science Foundation of China
The SUTD Digital Manufacturing and Design (DManD) Centre which is supported by the National Research Foundation (NRF) of Singapore
UMass Boston StartUp Grant
Singapore MOE Academic Research Fund
The National Research Foundation, Prime Minister's Office, Singapore under its IDM Futures Funding Initiative
Joseph P. Healey Research Grant, Office of the President, and the Office of the Vice Provost for Research and Strategic Initiatives & Dean of Graduate Studies of UMass Boston

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https://doi.org/10.1016/j.cag.2025.104175
Lu ZLi YWang F(2025)Complex layout generation for large-scale floor plans via deep edge-aware GNNsApplied Intelligence10.1007/s10489-025-06311-w55:6Online publication date: 1-Apr-2025
https://dl.acm.org/doi/10.1007/s10489-025-06311-w
Makinoshima FTakahashi TOishi YDastani MSichman JAlechina NDignum V(2024)Bayesian Behavioural Model Estimation for Live Crowd SimulationProceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems10.5555/3635637.3662994(1355-1362)Online publication date: 6-May-2024
https://dl.acm.org/doi/10.5555/3635637.3662994
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
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Lu ZGuan XYan DLi YHuang T(2024)Crowd behavior reconstruction with deep group feature learning2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)10.1109/BIBM62325.2024.10821834(5699-5706)Online publication date: 3-Dec-2024
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Yu ZZhang GLi YJin DFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Understanding and Modeling Collision Avoidance Behavior for Realistic Crowd SimulationProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615098(3052-3061)Online publication date: 21-Oct-2023
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