Kieu, L-M, Malleson, N and Heppenstall, A orcid.org/0000-0002-0663-3437 (2020) Dealing with uncertainty in agent-based models for short-term predictions. Royal Society Open Science, 7 (1). 191074. ISSN 2054-5703
Abstract
Agent-based models (ABMs) are gaining traction as one of the most powerful modelling tools within the social sciences. They are particularly suited to simulating complex systems. Despite many methodological advances within ABM, one of the major drawbacks is their inability to incorporate real-time data to make accurate short-term predictions. This paper presents an approach that allows ABMs to be dynamically optimized. Through a combination of parameter calibration and data assimilation (DA), the accuracy of model-based predictions using ABM in real time is increased. We use the exemplar of a bus route system to explore these methods. The bus route ABMs developed in this research are examples of ABMs that can be dynamically optimized by a combination of parameter calibration and DA. The proposed model and framework is a novel and transferable approach that can be used in any passenger information system, or in an intelligent transport systems to provide forecasts of bus locations and arrival times.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited. |
Keywords: | agent-based modelling; data assimilation; model calibration; complex systems |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > School of Geography (Leeds) > Centre for Spatial Analysis & Policy (Leeds) |
Funding Information: | Funder Grant number ESRC (Economic and Social Research Council) ES/R007918/1 Alan Turing Institute Not Known |
Depositing User: | Symplectic Publications |
Date Deposited: | 16 Dec 2019 11:35 |
Last Modified: | 25 Jun 2023 22:05 |
Status: | Published |
Publisher: | The Royal Society |
Identification Number: | 10.1098/rsos.191074 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:154609 |