Clay, R, Kieu, L-M, Ward, JA orcid.org/0000-0002-2469-7768 et al. (2 more authors) (2020) Towards Real-Time Crowd Simulation Under Uncertainty Using an Agent-Based Model and an Unscented Kalman Filter. In: Advances in Practical Applications of Agents, Multi-Agent Systems, and Trustworthiness. The PAAMS Collection. 18th International Conference on Practical Applications of Agents and Multi-Agent Systems (PAAMS 2020), 07-09 Oct 2020, L'Aquila, Italy. Springer , pp. 68-79. ISBN 9783030497774
Abstract
Agent-based modelling (ABM) is ideally suited to simulating crowds of people as it captures the complex behaviours and interactions between individuals that lead to the emergence of crowding. Currently, it is not possible to use ABM for real-time simulation due to the absence of established mechanisms for dynamically incorporating real-time data. This means that, although models are able to perform useful offline crowd simulations, they are unable to simulate the behaviours of crowds in real time. This paper begins to address this drawback by demonstrating how a data assimilation algorithm, the Unscented Kalman Filter (UKF), can be used to incorporate pseudo-real data into an agent-based model at run time. Experiments are conducted to test how well the algorithm works when a proportion of agents are tracked directly under varying levels of uncertainty. Notably, the experiments show that the behaviour of unobserved agents can be inferred from the behaviours of those that are observed. This has implications for modelling real crowds where full knowledge of all individuals will never be known. In presenting a new approach for creating real-time simulations of crowds, this paper has important implications for the management of various environments in global cities, from single buildings to larger structures such as transportation hubs, sports stadiums, through to entire city regions.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © Springer Nature Switzerland AG 2020. This is an author produced version of a conference paper published in Advances in Practical Applications of Agents, Multi-Agent Systems, and Trustworthiness. The PAAMS Collection. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Agent-based modelling; Unscented Kalman Filter; Uncertainty; Data assimilation; Crowd simulation |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mathematics (Leeds) > Applied Mathematics (Leeds) The University of Leeds > Faculty of Environment (Leeds) > School of Geography (Leeds) > Centre for Spatial Analysis & Policy (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 23 Oct 2020 14:48 |
Last Modified: | 23 Oct 2020 14:48 |
Status: | Published |
Publisher: | Springer |
Identification Number: | 10.1007/978-3-030-49778-1_6 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:167084 |