Pedestrian behavior and agent-based simulation modeling in public space

Abstract

Owing to the COVID-19 pandemic, people must practice social distancing (1.5 m) in public spaces. This restricts activities in such spaces, and traditional festivals are canceled. This study mainly focused on the COVID-19 outbreak and pedestrian gathering in public spaces and build up a simulation framework. Additionally, it examined pedestrian trajectory data to know the human behavior and seated the characteristic of the pedestrian. Agent-based modeling can make the agent in several behaviors know how they move in the space. The analysis and evaluation show that it's difficult in maintaining social distancing without protection stuff in public spaces. Subsequent studies are planned for different types of areas to generate and evaluate a variety of high-fidelity simulation patterns across several fields.