Do Groups Matter? An Agent-based Modeling Approach to Pedestrian Egress

Collins, Andrew J.; Elzie, Terra; Frydenlund, Erika; Robinson, R. Michael

doi:10.1016/j.trpro.2014.09.051

Cited by 19 publications

(12 citation statements)

References 10 publications

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“…Interactive learning can be done in multiple ways, i.e., based on different social learning strategies [33]. Agents might be represented as members of local groups, learning together and mimicking behavior from other group members (i.e., collective learning) [34]. Yet, the impact of different types of interactive learning in groups compared to learning by an individual is an under-explored domain in the development of ABMs of socio-environmental systems.…”

Section: Introductionmentioning

confidence: 99%

Risk perception and behavioral change during epidemics: Comparing models of individual and collective learning

et al. 2020

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Modern societies are exposed to a myriad of risks ranging from disease to natural hazards and technological disruptions. Exploring how the awareness of risk spreads and how it triggers a diffusion of coping strategies is prominent in the research agenda of various domains. It requires a deep understanding of how individuals perceive risks and communicate about the effectiveness of protective measures, highlighting learning and social interaction as the core mechanisms driving such processes. Methodological approaches that range from purely physics-based diffusion models to data-driven environmental methods rely on agentbased modeling to accommodate context-dependent learning and social interactions in a diffusion process. Mixing agent-based modeling with data-driven machine learning has become popularity. However, little attention has been paid to the role of intelligent learning in risk appraisal and protective decisions, whether used in an individual or a collective process. The differences between collective learning and individual learning have not been sufficiently explored in diffusion modeling in general and in agent-based models of socioenvironmental systems in particular. To address this research gap, we explored the implications of intelligent learning on the gradient from individual to collective learning, using an agent-based model enhanced by machine learning. Our simulation experiments showed that individual intelligent judgement about risks and the selection of coping strategies by groups with majority votes were outperformed by leader-based groups and even individuals deciding alone. Social interactions appeared essential for both individual learning and group learning. The choice of how to represent social learning in an agent-based model could be driven by existing cultural and social norms prevalent in a modeled society.

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Section: Introductionmentioning

confidence: 99%

Risk perception and behavioral change during epidemics: Comparing models of individual and collective learning

et al. 2020

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“…Prior research has shown that the presence of groups makes a difference in evacuation time (6,22). Pedestrian movement in models and simulations encompasses a range of factors related to groups, from individual mobility to behaviors adjusted to accommodate group configurations.…”

Section: Pedestrian Evacuationmentioning

confidence: 99%

Panic That Spreads Sociobehavioral Contagion in Pedestrian Evacuations

Elzie

Frydenlund

Collins

et al. 2016

Transportation Research Record: Journal of the Transportation R

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Crowds are a part of everyday public life, from stadiums and arenas to school hallways. Occasionally, pushing within the crowd spontaneously escalates to crushing behavior, resulting in injuries and even death. The rarity and unpredictability of these incidents provides few options to collect data for research on the prediction and prevention of hazardous emergent behaviors in crowds. This study takes a close look at the way states of agitation, such as panic, can spread through crowds. Group composition-mainly family groups composed of members with differing mobility levels-plays an important role in the spread of agitation through the crowd, ultimately affecting the exit density and evacuation clearance time of a simulated venue. This study used an agent-based model of pedestrian movement during the egress of a hypothetical room and adopted an emotional, cognitive, and social framework to explore the transference and dissipation of agitation through a crowd. The preliminary results reveal that average group size in a crowd is a primary contributor to the exit density and evacuation clearance time. The study provides the groundwork on which to build more elaborate models that incorporate sociobehavioral aspects to simulate human movement during panic situations and account for the potential for dangerous behavior to emerge in crowds.

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“…Liu et al, [13] and Chen and Xiao [14] used linear programming to evacuation route rerouting; Wang et al, [15] used systems dynamics to explore the spread of panic in evacuations; and qualitative studies of evacuees' decision processes was conducted by Robinson and Khattak [16]. Pedestrians are also model in evacuation simulations [17][18][19].…”

Section: Existing Evacuation Simulationsmentioning

confidence: 99%