Since March 2020, many premises have been relatively empty, but the government is keen to revitalise the economy. Against a rapidly changing backdrop of rules and guidelines to curb the spread of Covid-19 is the need to ensure that public buildings and premises can either safely remain open or plan to reopen as safely as possible. Rather than relying on a static two-dimensional image of a building layout, which can only show pinch points in terms of two dimensions and does not demonstrate how people will interact with the layout, crowd modelling more accurately reflects how people move within a building or public area.

Crowd modelling software can use conflict analysis to determine whether people are likely to come into contact or close proximity with one another. Conflict analysis can be used to identify pinch points in a building. In this case, the conflict zone is set at the regional requirement for social distancing, such as two metres for the UK. The simulation then models the crowd’s movements and identifies all possible conflict zones.

The overall formulation allows different stressors, expressed as functions of space and time, including time pressure, positional stressors, area stressors and inter-personal stressors. This model can be used to simulate dynamic crowd behaviors at interactive rates, including walking with variable speeds, breaking lane-formation over time and cutting through a normal flow. We also perform qualitative and quantitative comparisons between our simulation results and real-world observations.

We also derive a linear mapping between simulation parameters and personality descriptors corresponding to the well-established Eysenck three-factor personality model. Furthermore, we propose a novel two-dimensional factorization of perceived personality in crowds based on a statistical analysis of the user study results. Finally, we demonstrate that our mappings and factorizations can be used to generate heterogeneous crowd behaviors in different settings.

Moreover, the algorithm can automatically generate many emergent phenomena such as lane formation, crowd compression, edge and wake effects ant others. We compare the results from our simulations to data collected from prior studies in pedestrian and crowd dynamics, and provide visual comparisons with real-world video. In practice, our approach can interactively simulate large crowds with thousands of agents on a desktop PC and naturally generates a diverse set of emergent behaviours.

Regards
Sarah Rose
Managing Editor
International Journal of Swarm Intelligence and Evolutionary Computation