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Register a new userSocial Distance Characterization by means of Pedestrian Simulation
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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.
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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 traffic of pedestrians affects the number of walking or running people passing by each other. By applying certain rules this number can be significantly reduced, thus reducing the contribution of person-to-person contagious to the basic reproductive number, R0. One example is the traffic of pedestrians on sidewalks. Another is the use of walking or running tracks in parks. It is demonstrated here that the number of people crossing each other can be drastically reduced if one-way traffic is enforced and runners are separated from walkers.
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 huge number of free parameters as in the case of multilayer neural networks. Although the method is general, we focus on the one pedestrian-one obstacle problem. Experimental data were collected in a motion capture laboratory providing high-precision trajectories. The proposed model allows us to simulate the trajectory of a pedestrian avoiding an obstacle from any direction.
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 the well-known tendency of 3-member groups to be arranged in a V-shape. The existence of group structures is thus likely to impact the collective dynamics of the crowd, possibly in a critical way when emergency situations are considered. After turning a blind eye to these group aspects for years, endeavours to model groups in crowd simulation software have thrived in the past decades. This fairly short review opens on a description of their empirical characteristics and their impact on the global flow. Then, it aims to offer a pedagogical discussion of the main strategies to model such groups, within different types of models, in order to provide guidance for prospective modellers.
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 within large populations. As a consequence, often the quantity in need of calibration may be a distribution over the population whose relation with the parameters of the model is analytically intractable. Nevertheless, we can simulate. In this paper we present a simulation-based framework for the calibration of agent-based models with distributional output based on indirect inference. We illustrate our method step by step on a model of norm emergence in an online community of peer production, using data from three large Wikipedia communities. Model fit and diagnostics are discussed.
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 of such systems difficult to measure and to control. To quantify robustness, we propose a coreness value obtained from the directed interaction network. We study the emergence of large drop-out cascades of users leaving the OSN by means of an agent-based model. For agents, we define a utility function that depends on their relative reputation and their costs for interactions. The decision of agents to leave the OSN depends on this utility. Our aim is to prevent drop-out cascades by influencing specific agents with low utility. We identify strategies to control agents in the core and the periphery of the OSN such that drop-out cascades are significantly reduced, and the robustness of the OSN is increased.
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