research-article

Characterizing the relationship between environment layout and crowd movement using machine learning

Authors:

Vladimir Pavlovic,

Petros Faloutsos,

Mubbasir KapadiaAuthors Info & Claims

MIG '17: Proceedings of the 10th International Conference on Motion in Games

Article No.: 2, Pages 1 - 6

https://doi.org/10.1145/3136457.3136474

Published: 08 November 2017 Publication History

Abstract

Crowd simulations facilitate the study of how an environment layout impacts the movement and behavior of its inhabitants. However, simulations are computationally expensive, which make them infeasible when used as part of interactive systems (e.g., Computer-Assisted Design software). Machine learning models, such as neural networks (NN), can learn observed behaviors from examples, and can potentially offer a rational prediction of a crowd's behavior efficiently. To this end, we propose a method to predict the aggregate characteristics of crowd dynamics using regression neural networks (NN). We parametrize the environment, the crowd distribution and the steering method to serve as inputs to the NN models, while a number of common performance measures serve as the output. Our preliminary experiments show that our approach can help users evaluate a large number of environments efficiently.

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Cited By

Ren JXiang WXiao YYang RManocha DJin X(2021)Heter-Sim: Heterogeneous Multi-Agent Systems Simulation by Interactive Data-Driven OptimizationIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2019.294676927:3(1953-1966)Online publication date: 1-Mar-2021
https://doi.org/10.1109/TVCG.2019.2946769
Musse SCassol VThalmann D(2021)A history of crowd simulation: the past, evolution, and new perspectivesThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-021-02252-w37:12(3077-3092)Online publication date: 5-Aug-2021
https://dl.acm.org/doi/10.1007/s00371-021-02252-w
Qiao GZhou HKapadia MYoon SPavlovic V(2019)Scenario Generalization of Data-driven Imitation Models in Crowd SimulationProceedings of the 12th ACM SIGGRAPH Conference on Motion, Interaction and Games10.1145/3359566.3360087(1-11)Online publication date: 28-Oct-2019
https://dl.acm.org/doi/10.1145/3359566.3360087
Show More Cited By

Index Terms

Characterizing the relationship between environment layout and crowd movement using machine learning
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks
  2. Modeling and simulation

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cover image ACM Conferences

MIG '17: Proceedings of the 10th International Conference on Motion in Games

November 2017

128 pages

ISBN:9781450355414

DOI:10.1145/3136457

Conference Chair:
Nuria Pelechano
Universitat Politecnica de Catalunya
,
Editor:
Stephen N. Spencer
University of Washington
,
Program Chairs:
Carol O'Sullivan
Trinity College Dublin
,
Julien Pettré
INRIA Rennes

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 08 November 2017

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The College of New Jersey

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MiG '17

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SIGGRAPH

MiG '17: Motion in Games

November 8 - 10, 2017

Barcelona, Spain

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Overall Acceptance Rate -9 of -9 submissions, 100%

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MIG '24

Sponsor:
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The 17th ACM SIGGRAPH Conference on Motion, Interaction, and Games

November 21 - 23, 2024

Arlington , VA , USA

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Cited By

Ren JXiang WXiao YYang RManocha DJin X(2021)Heter-Sim: Heterogeneous Multi-Agent Systems Simulation by Interactive Data-Driven OptimizationIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2019.294676927:3(1953-1966)Online publication date: 1-Mar-2021
https://doi.org/10.1109/TVCG.2019.2946769
Musse SCassol VThalmann D(2021)A history of crowd simulation: the past, evolution, and new perspectivesThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-021-02252-w37:12(3077-3092)Online publication date: 5-Aug-2021
https://dl.acm.org/doi/10.1007/s00371-021-02252-w
Qiao GZhou HKapadia MYoon SPavlovic V(2019)Scenario Generalization of Data-driven Imitation Models in Crowd SimulationProceedings of the 12th ACM SIGGRAPH Conference on Motion, Interaction and Games10.1145/3359566.3360087(1-11)Online publication date: 28-Oct-2019
https://dl.acm.org/doi/10.1145/3359566.3360087
Testa EBarros RMusse S(2019)CrowdEstThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-019-01684-935:6-8(1119-1130)Online publication date: 1-Jun-2019
https://dl.acm.org/doi/10.1007/s00371-019-01684-9
Schaumann DKapadia MDavis PO'Mahony APfautz J(2019)Modeling Social and Spatial Behavior in Built Environments: Current Methods and Future DirectionsSocial‐Behavioral Modeling for Complex Systems10.1002/9781119485001.ch29(673-695)Online publication date: 29-Mar-2019
https://doi.org/10.1002/9781119485001.ch29
Lee JWon JLee JCharalambous PChrysanthou YJones BLee J(2018)Crowd simulation by deep reinforcement learningProceedings of the 11th ACM SIGGRAPH Conference on Motion, Interaction and Games10.1145/3274247.3274510(1-7)Online publication date: 8-Nov-2018
https://dl.acm.org/doi/10.1145/3274247.3274510
Mathew CKnob PMusse SAliaga D(2018)Urban Walkability Design Using Virtual Population SimulationComputer Graphics Forum10.1111/cgf.1358538:1(455-469)Online publication date: 8-Oct-2018
https://doi.org/10.1111/cgf.13585

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