Simulating crowds in real time with agent-based modelling and a particle filter

Malleson, N., Minors, K., Kieu, L.-M., Ward, J. A., West, A. and Heppenstall, A. (2020) Simulating crowds in real time with agent-based modelling and a particle filter. Journal of Artificial Societies and Social Simulation, 23(3), p. 3. (doi: 10.18564/jasss.4266)

Abstract

Agent-based modelling is a valuable approach for modelling systems whose behaviour is driven by the interactions between distinct entities, such as crowds of people. However, it faces a fundamental difficulty: there are no established mechanisms for dynamically incorporating real-time data into models. This limits simulations that are inherently dynamic, such as those of pedestrian movements, to scenario testing on historic patterns rather than real-time simulation of the present. This paper demonstrates how a particle filter could be used to incorporate data into an agent-based model of pedestrian movements at run time. The experiments show that although it is possible to use a particle filter to perform online (real time) model optimisation, the number of individual particles required (and hence the computational complexity) increases exponentially with the number of agents. Furthermore, the paper assumes a one-to-one mapping between observations and individual agents, which would not be the case in reality. Therefore this paper lays some of the fundamental groundwork and highlights the key challenges that need to be addressed for the real-time simulation of crowd movements to become a reality. Such success could have implications for the management of complex environments both nationally and internationally such as transportation hubs, hospitals, shopping centres, etc.

• Item Type: Articles
• Additional 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), through a UK Economic and Social Research Council (ESRC) Future Research Leaders grant [number ES/L009900/1], 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: Malleson, N., Minors, K., Kieu, L.-M., Ward, J. A., West, A., and Heppenstall, A.
• College/School: College of Social Sciences > School of Social and Political Sciences > Urban Studies
• Journal Name: Journal of Artificial Societies and Social Simulation
• Publisher: SimSoc Consortium
• ISSN: 1460-7425
• ISSN (Online): 1460-7425
• Copyright Holders: Copyright © 2020 The Author(s)
• First Published: First published in Journal of Artificial Societies and Social Simulation 23(3):3
• Publisher Policy: Reproduced under a creative commons licence