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In the present work, we study how the number of simulated clients (occupancy) affects the social distance in an ideal supermarket. For this, we account for realistic typical dimensions and process time (picking products and checkout). From the simulated trajectories, we measure events of social distance less than 2 m and its duration. Between other observables, we define a social distance coefficient that informs how many events (of a given duration) suffer each agent in the system. These kinds of outputs could be useful for building procedures and protocols in the context of a pandemic allowing to keep low health risks while setting a maximum operating capacity.
Minimizing social contact is an important tool to reduce the spread of diseases, but harms peoples well-being. This and other, more compelling reasons, urge people to walk outside periodically. The present simulation explores how organizing the traff
Data-driven simulation of pedestrian dynamics is an incipient and promising approach for building reliable microscopic pedestrian models. We propose a methodology based on generalized regression neural networks, which does not have to deal with a hug
Pedestrians are often encountered walking in the company of some social relations, rather than alone. The social groups thus formed, in variable proportions depending on the context, are not randomly organised but exhibit distinct features, such as t
Simulation with agent-based models is increasingly used in the study of complex socio-technical systems and in social simulation in general. This paradigm offers a number of attractive features, namely the possibility of modeling emergent phenomena w
Online social networks (OSN) are prime examples of socio-technical systems in which individuals interact via a technical platform. OSN are very volatile because users enter and exit and frequently change their interactions. This makes the robustness