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Agent-based models (ABMs) offer promise for realistically simulating human behaviours and interactions during emergency evacuations. This review aims to systematically assess the state of the art in ABM-based evacuation modelling with respect to methodologies, validation practices, and the associated challenges over the past decade. The review critically examines 134 studies from 2013 to 2023 that have applied ABMs for pedestrian evacuation simulation to synthesise current capabilities, limitations, and advancement pathways. Findings identify persistent challenges related to modeller bias, computational complexity, data scarcity for calibration and validation, and the predominance of simplistic rule-based decision-making models, while promise exists with the adoption of flexible behavioural frameworks, high-performance computing architectures, machine learning techniques for adaptive agent behaviours and surrogate modelling, and evolutionary computation methods for transparent rule generation. The findings underscore the importance of interdisciplinary collaboration among behavioural scientists, modellers, and emergency planners to enhance the realism and reliability of ABMs. By providing a critical synthesis of the state-of-the-art and proposing future research directions, this review aims to accelerate the development and application of ABMs that can meaningfully enhance the safety and resilience of communities facing emergencies.
Senanayake et al. (Fri,) studied this question.
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