Real-time agent-based crowd simulation with the Reversible Jump Unscented Kalman Filter

Clay, Robert L.; Ward, Jonathan M.; Ternes, Patricia; Kieu, Le Minh; Malleson, Nick

doi:10.1016/j.simpat.2021.102386

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Cited by 5 publications

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“…This type of variable is extremely common in ABM. Therefore attempts are underway to include categorical variables through innovations such as the Reversible Jump, which may allow multiple filters to try to find the 'correct' categorical parameter values [67], or by resampling only some model variables [37]. As Section 10 will discuss, the ultimate aim of these efforts is to allow for real-time agent-based models of dynamic urban systems, drawing on diverse data sources and making accurate short-term predictions to aid policy making.…”

Section: Real-time Abmmentioning

confidence: 99%

Agent-based modelling for Urban Analytics: State of the art and challenges

Malleson

Birkin

Birks

et al. 2022

AIC

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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.

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Real-time agent-based crowd simulation with the Reversible Jump Unscented Kalman Filter

Cited by 5 publications

References 44 publications

Agent-based modelling for Urban Analytics: State of the art and challenges

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