Face-Validation of a Route-Choice Module in a Crowd Simulator for Confined Indoor Spaces in Context of the COVID-19 Pandemic

The COVID-19 pandemic has drastically changed the life of most people in the world. Governments have come up with various restrictions and measures to contain virus spreading in public (confined) indoor spaces. Simulation tools can reproduce people behaviour and assess how people react on different intervention measures, and are of high value for the related stakeholders. This research extends an existing microscopic pedestrian simulation model for this purpose, where three behavioural decision (strategic, tactical, and operational) levels are considered and adapted to capture crowd movement dynamics in a new context, i.e., indoor locomotion in tight spaces. To validate the newly developed modules, empirical data is collected in real life. This paper presents the effort of collecting empirical data that describe pedestrian route choice behaviour in one of the typical indoor spaces - restaurants. The face-validation for the corresponding route choice module has been conducted. Our study reveals influential factors (i.e., size, location) and behavioural insights regarding tactical decisions in the chosen confined space.