Data assimilation and agent-based modelling: towards the incorporation of categorical agent parameters

Ternes, P., Ward, J. A., Heppenstall, A. , Kumar, V., Kieu, L.-M. and Malleson, N. (2022) Data assimilation and agent-based modelling: towards the incorporation of categorical agent parameters. Open Research Europe, 1, 131. (doi: 10.12688/openreseurope.14144.2)

Abstract

This paper explores the use of a particle filter—a data assimilation method—to incorporate real-time data into an agent-based model. We apply the method to a simulation of real pedestrians moving through the concourse of Grand Central Terminal in New York City (USA). The results show that the particle filter does not perform well due to (i) the unpredictable behaviour of some pedestrians and (ii) because the filter does not optimise the categorical agent parameters that are characteristic of this type of model. This problem only arises because the experiments use real-world pedestrian movement data, rather than simulated, hypothetical data, as is more common. We point to a potential solution that involves resampling some of the variables in a particle, such as the locations of the agents in space, but keeps other variables such as the agents’ choice of destination. This research illustrates the importance of including real-world data and provides a proof of concept for the application of an improved particle filter to an agent-based model. The obstacles and solutions discussed have important implications for future work that is focused on building large-scale real-time agent-based models.

• Item Type: Articles
• Additional Information: Version 2; peer review: 1 approved, 1 approved with reservations. Grant information: 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), and through an internship funded by the UK Leeds Institute for Data Analytics (LIDA).
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Heppenstall, Professor Alison
• Authors: Ternes, P., Ward, J. A., Heppenstall, A., Kumar, V., Kieu, L.-M., and Malleson, N.
• College/School: College of Social Sciences > School of Social and Political Sciences
• Journal Name: Open Research Europe
• Publisher: F1000Research
• ISSN: 2732-5121
• ISSN (Online): 2732-5121
• Published Online: 27 October 2021
• Copyright Holders: Copyright: © 2022 Ternes P et al
• First Published: First published in Open Research Europe 1:131
• Publisher Policy: Reproduced under a Creative Commons licence