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Simulating Crowds in Real Time with Agent-Based Modelling and a Particle Filter

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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.

Suggested Citation

  • Nicolas Malleson & Kevin Minors & Le-Minh Kieu & Jonathan Ward & Andrew West & Alison Heppenstall, 2020. "Simulating Crowds in Real Time with Agent-Based Modelling and a Particle Filter," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 23(3), pages 1-3.
  • Handle: RePEc:jas:jasssj:2019-158-2
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    1. Francis Oloo & Kamran Safi & Jagannath Aryal, 2018. "Predicting Migratory Corridors of White Storks, Ciconia ciconia , to Enhance Sustainable Wind Energy Planning: A Data-Driven Agent-Based Model," Sustainability, MDPI, vol. 10(5), pages 1-22, May.
    2. J. Doyne Farmer & Duncan Foley, 2009. "The economy needs agent-based modelling," Nature, Nature, vol. 460(7256), pages 685-686, August.
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    Cited by:

    1. Farmer, J. Doyne & Dyer, Joel & Cannon, Patrick & Schmon, Sebastian, 2022. "Black-box Bayesian inference for economic agent-based models," INET Oxford Working Papers 2022-05, Institute for New Economic Thinking at the Oxford Martin School, University of Oxford.
    2. Dyer, Joel & Cannon, Patrick & Farmer, J. Doyne & Schmon, Sebastian M., 2024. "Black-box Bayesian inference for agent-based models," Journal of Economic Dynamics and Control, Elsevier, vol. 161(C).
    3. Oswald, Yannick & Suchak, Keiran & Malleson, Nick, 2025. "Agent-based models of the United States wealth distribution with Ensemble Kalman Filter," Journal of Economic Behavior & Organization, Elsevier, vol. 229(C).
    4. An, Li & Grimm, Volker & Sullivan, Abigail & Turner II, B.L. & Malleson, Nicolas & Heppenstall, Alison & Vincenot, Christian & Robinson, Derek & Ye, Xinyue & Liu, Jianguo & Lindkvist, Emilie & Tang, W, 2021. "Challenges, tasks, and opportunities in modeling agent-based complex systems," Ecological Modelling, Elsevier, vol. 457(C).

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