Evaluating safety in museums during pandemic emergencies. An integrated approach between Space Syntax and an AI-based analysis of visitors' flows

The COVID pandemic emergency has brought changes in the use of highly crowded public buildings, including large museums, which, upon reopening to the public, have had to manage visitor flow strategies that ensure social security and distance. However, while effective, these measures have often compromised the quality of the visitor experience, resulting in a restricted and less inclusive fruition experience. Hence, to address future emergencies, it's crucial to apply lessons learned from COVID research on space and visitor management. In this context, several studies have considered the application of methodologies and tools to analyze visitor movements in museums. In particular, the "indirect" methodology referring to configurational analysis (Space Syntax) [1, 2] emerges as one of the most effective in interpreting the role of spatial configuration and characterization in guiding user movement [3], facilitating the post pandemic reopening of important international museums [4]. Simultaneously, a more "direct" approach utilizes modern AI technologies to collect and analyze visitor flow data [5, 6]. Examining the pros and cons of each of the two approaches, this contribution aims to evaluate an integrated approach using Museo di Roma at Palazzo Braschi as a case study. By combining these methodologies, the goal is to gather comprehensive data for evaluating strategies that ensure safety, optimize visitor experiences, and enhance cultural heritage appreciation.