Clay, R, Ward, JA orcid.org/0000-0002-2469-7768, Ternes, P et al. (2 more authors) (2021) Real-time agent-based crowd simulation with the Reversible Jump Unscented Kalman Filter. Simulation Modelling Practice and Theory, 113. 102386. ISSN 1569-190X
Abstract
Commonly-used data assimilation methods are being adapted for use with agent-based models with the aim of allowing optimisation in response to new data in real-time. However, existing methods face difficulties working with categorical parameters, which are common in agent-based models. This paper presents a new method, the RJUKF, that combines the Unscented Kalman Filter (UKF) data assimilation algorithm with elements of the Reversible Jump (RJ) Markov chain Monte Carlo method. The proposed method is able to conduct data assimilation on both continuous and categorical parameters simultaneously. Compared to similar techniques for mixed state estimation, the RJUKF has the advantage of being efficient enough for online (i.e. real-time) application. The new method is demonstrated on the simulation of a crowd of people traversing a train station and is able to estimate both their current position (a continuous, Gaussian variable) and their chosen destination (a categorical parameter). This method makes a valuable contribution towards the use of agent-based models as tools for the management of crowds in busy places such as public transport hubs, shopping centres, or high streets.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 Elsevier B.V. All rights reserved. This is an author produced version of an article published in Simulation Modelling Practice and Theory. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Agent-based modelling; Data assimilation; Unscented Kalman filter; Crowd simulation; MCMC |
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) |
Funding Information: | Funder Grant number Alan Turing Institute Not Known |
Depositing User: | Symplectic Publications |
Date Deposited: | 29 Oct 2021 08:07 |
Last Modified: | 08 Aug 2022 00:13 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.simpat.2021.102386 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:179696 |