Intelligent agent model for simulating pedestrian traffic at urban trade fairs during the COVID-19 pandemic

Abstract

The COVID-19 pandemic highlighted the need for new approaches to the urban environment. Among them, physical distancing is intended to minimize contagion and safeguard general welfare. In the present research, a simulated model was designed to identify close contacts during pedestrian traffic in specific urban activities such as a trade fair. This research type was applied and of quasi-experimental design, a parametric simulation model based on intelligent agents was developed at the micro level in urban trade fair scenarios. Initially, theoretical trade fair configurations were compared, considering the shape of the walkable space and the arrangement of trade stands. Then, a manipulation of particular parameters is performed, among them, the probability of making a stall visible and the probability of stopping in case of direct contact or collision. Finally, the results were compared using statistical correlations, the PerMANOVA test, Games-Howell and the graphical analysis of the micro-simulated pedestrian behaviors by the developed model. It is concluded that the model is valid for theoretical pedestrian traffic models for the five simulated urban fairground scenarios.