Markets Post Covid-19: Agent-Based Computational Validation Methodology For Urban Interventions On Spontaneous "Informal Street Markets" In Public Spaces

Abstract

The Covid-19 health crisis has turned spontaneous "informal street markets" into dangerous hotspots for the spread of Covid-19 due to the formation of crowds of people. These informal markets are due to a lack of state planning and regulation, a reality that exists throughout Latin America. This research aims to analyse these spaces through a methodology for computational validation that uses an agent-based model (ABM) for the abstraction and simulation of the displacement of people (moving agents) and their behaviour in the spatial configuration of the area (static agents), identifying an aggregated score in each simulation with the purpose of designing urban interventions that reduce the probability of forming crowds. The paper presents the proposed methodology and the ABM with a preliminary validation by simulating two spatial configurations with two hypothetical scenarios (analyses with 10 and 50 agents) and comparing their aggregated scores, showing a correlation between spatial configuration with the formation of crowds.