Abstract
Agent-based modelling (ABM) is ideally suited to simulating crowds of people as it captures the complex behaviours and interactions between individuals that lead to the emergence of crowding. Currently, it is not possible to use ABM for real-time simulation due to the absence of established mechanisms for dynamically incorporating real-time data. This means that, although models are able to perform useful offline crowd simulations, they are unable to simulate the behaviours of crowds in real time. This paper begins to address this drawback by demonstrating how a data assimilation algorithm, the Unscented Kalman Filter (UKF), can be used to incorporate pseudo-real data into an agent-based model at run time. Experiments are conducted to test how well the algorithm works when a proportion of agents are tracked directly under varying levels of uncertainty. Notably, the experiments show that the behaviour of unobserved agents can be inferred from the behaviours of those that are observed. This has implications for modelling real crowds where full knowledge of all individuals will never be known. In presenting a new approach for creating real-time simulations of crowds, this paper has important implications for the management of various environments in global cities, from single buildings to larger structures such as transportation hubs, sports stadiums, through to entire city regions.
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], an ESRC-Alan Turing Fellowship [number ES/R007918/1] and is part of the Leeds Institute for Data Analytics (LIDA) Data Scientist Internship Programme 2018/19, funded by the Alan Turing Institute.
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Notes
- 1.
- 2.
The StationSim model, specifically, can be found at: https://git.io/JvJSm. The code to run the experiments conducted here can be found at https://git.io/JvJSq.
- 3.
This work was undertaken on ARC3, part of the High Performance Computing facilities at the University of Leeds, UK.
- 4.
In practice, noise assumptions can be tailored to improve performance, but under high dimensional scenarios such as this it can prove difficult to optimise. This provides a strong motivation for further adaptions to the UKF particularly adaptive filtering [1].
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Clay, R., Kieu, LM., Ward, J.A., Heppenstall, A., Malleson, N. (2020). Towards Real-Time Crowd Simulation Under Uncertainty Using an Agent-Based Model and an Unscented Kalman Filter. In: Demazeau, Y., Holvoet, T., Corchado, J., Costantini, S. (eds) Advances in Practical Applications of Agents, Multi-Agent Systems, and Trustworthiness. The PAAMS Collection. PAAMS 2020. Lecture Notes in Computer Science(), vol 12092. Springer, Cham. https://doi.org/10.1007/978-3-030-49778-1_6
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