Kieu, M, Nguyen, H, Ward, JA orcid.org/0000-0002-2469-7768 et al. (1 more author) (2022) Towards real-time predictions using emulators of agent-based models. Journal of Simulation. ISSN 1747-7778
Abstract
The use of Agent-Based Models (ABMs) to make predictions in real-time is hindered by their high computation cost and the lack of detailed individual data. This paper proposes a new framework to enable the use of emulators, also referred to as surrogate models or meta-models, coupled with ABMs, to allow for real-time predictions of the behaviour of a complex system. The case study is that of pedestrian movements through an environment. We evaluate two different types of emulators: a regression emulator based on a Random Forest and a time-series emulator using a Long Short-Term Memory neural network. Both emulators perform well, but the time-series emulator proves to generalise better to cases where the number of agents in the system is not known a priori. The results have implications for the real-time modelling of human crowds, suggesting that emulation is a feasible approach to modelling crowds in real-time, where computational complexity prohibits the use of an ABM directly.
Metadata
Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 The Operational Research Society. This is an author produced version of an article published in Journal of Simulation. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Agent-based modelling; emulators; meta-modelling; machine learning |
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: | 04 Jul 2022 13:16 |
Last Modified: | 05 Jun 2023 00:13 |
Status: | Published online |
Publisher: | Taylor and Francis |
Identification Number: | https://doi.org/10.1080/17477778.2022.2080008 |
Related URLs: |