Agent-based modelling for urban analytics: state of the art and challenges

Malleson, N., Birkin, M., Birks, D., Ge, J., Heppenstall, A. , Manley, E., McCulloch, J. and Ternes, P. (2022) Agent-based modelling for urban analytics: state of the art and challenges. AI Communications, 35(4), pp. 393-406. (doi: 10.3233/AIC-220114)

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Agent-based modelling (ABM) is a facet of wider Multi-Agent Systems (MAS) research that explores the collective behaviour of individual ‘agents’, and the implications that their behaviour and interactions have for wider systemic behaviour. The method has been shown to hold considerable value in exploring and understanding human societies, but is still largely confined to use in academia. This is particularly evident in the field of Urban Analytics; one that is characterised by the use of new forms of data in combination with computational approaches to gain insight into urban processes. In Urban Analytics, ABM is gaining popularity as a valuable method for understanding the low-level interactions that ultimately drive cities, but as yet is rarely used by stakeholders (planners, governments, etc.) to address real policy problems. This paper presents the state-of-the-art in the application of ABM at the interface of MAS and Urban Analytics by a group of ABM researchers who are affiliated with the Urban Analytics programme of the Alan Turing Institute in London (UK). It addresses issues around modelling behaviour, the use of new forms of data, the calibration of models under high uncertainty, real-time modelling, the use of AI techniques, large-scale models, and the implications for modelling policy. The discussion also contextualises current research in wider debates around Data Science, Artificial Intelligence, and MAS more broadly.

Item Type:Articles
Additional Information:This work in this project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 757455), UKPRP (MR/S037578/2), Medical Research Council (MC/_UU/00022/5) and Scottish Government Chief Scientist Office (SPHSU20). It was also supported by Wave 1 of The UKRI Strategic Priorities Fund (EPSRC Grant EP/T001569/1), specifically within the Criminal Justice System theme of the Alan Turing Institute’s research activities, and the Turing’s ASG programme (EP/T001569/1, Strategic Priorities Fund - AI for Science, Engineering, Health and Government).
Glasgow Author(s) Enlighten ID:Heppenstall, Professor Alison
Authors: Malleson, N., Birkin, M., Birks, D., Ge, J., Heppenstall, A., Manley, E., McCulloch, J., and Ternes, P.
College/School:College of Social Sciences > School of Social and Political Sciences
Journal Name:AI Communications
Publisher:IOS Press
ISSN (Online):1875-8452
Published Online:20 September 2022
Copyright Holders:Copyright © IOS Press 2022
First Published:First published in AI Communications 35(4): 393-406
Publisher Policy:Reproduced in accordance with the publisher copyright policy

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
313944System-science Informed Public Health and Economic Research for non-communicable Disease Prevention (the SIPHER consortium)Petra MeierMedical Research Council (MRC)MR/S037578/2HW - MRC/CSO Social and Public Health Sciences Unit
3048231Systems science research in public healthPetra MeierOffice of the Chief Scientific Adviser (CSO)SPHSU20HW - MRC/CSO Social and Public Health Sciences Unit