Dealing with uncertainty in agent-based models for short-term predictions

Kieu, L.-M., Malleson, N. and Heppenstall, A. (2020) Dealing with uncertainty in agent-based models for short-term predictions. Royal Society Open Science, 7(1), 191074. (doi: 10.1098/rsos.191074) (PMID:32218939) (PMCID:PMC7029931)

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.

• Item Type: Articles
• Additional Information: This project has received funding from the European Research Council (ERC) under the European Union Horizon 2020research and innovation programme (grant agreement no. 757455), a UK Economic and Social Research Council (ESRC) Future Research Leaders grant no. (ES/L009900/1) and an ESRC/Alan Turing Joint Fellowship (ES/R007918/1).
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Heppenstall, Professor Alison
• Authors: Kieu, L.-M., Malleson, N., and Heppenstall, A.
• College/School: College of Social Sciences > School of Social and Political Sciences > Urban Studies
• Journal Name: Royal Society Open Science
• Publisher: The Royal Society
• ISSN: 2054-5703
• ISSN (Online): 2054-5703
• Published Online: 15 January 2020
• Copyright Holders: Copyright © 2020 The Author(s)
• First Published: First published in Royal Society Open Science 7(1):191074
• Publisher Policy: Reproduced under a creative commons licence