Enhancing Museum Experience by Interactive Visitor Flow Analytics

This study presented the development and evaluation of a sensor-based, data-driven system for analyzing visitor behavior within a museum environment.Using historical data collected by strategically placed sensors, the system visualizes both spatial and temporal patterns of visitor flow through interactive heatmaps and charts.The system adopts a quantitative research approach, supported by Python-based data processing and React-powered web visualizations.A coordinated multi-view interface enables users to interact with different visual components, providing detailed, dynamic insights into visitor distribution and movement patterns.Although the current implementation utilizes historical data, the system is designed to accommodate real-time data once sensors are installed.The results revealed significant variations in visitor density across rooms and times, supporting operational decisions related to crowd management, exhibit placement, and visitor experience optimization.The integration of dynamic visualization tools offers museum staff a more efficient, informed approach to managing visitor flows and improving overall operational efficiency.