Multi-Spatial-Parametric evacuation modeling for data mining of route selection in University Libraries: an immersive VR-based approach

Abstract

The evacuation performance of university libraries directly impacts the safety of students’ lives during emergencies. Accurate evacuation models can provide evacuation design with reliable evidence for decision-making. While the Original Evacuation Model (OEM) relies solely on distance-based route selection, they ignore critical spatial parameters that influence human behavior. In this study, therefore, a Refined Evacuation Model (REM) with multi-spatial-parameter-based route selection logic has been developed through immersive virtual reality experiments, focusing on circulation spaces comprising corridors and open spaces (wider than corridors) in university libraries. The physiological data were collected to explain the route selection process, and the results indicated that the left–right positioning and width of open spaces significantly influence path selection in cases with equal distance. The REM models these behavioral patterns as rule-based logic, correcting the OEM evacuation time by up to 46.43%. Case studies show that widening right-side open spaces or narrowing left-side ones could reduce evacuation time. This strategic layout can shorten the evacuation time by up to 31.71%. This study bridges behavioral knowledge with computational modeling and provides a framework for knowledge-intensive evacuation design. It can be used as a practical tool for architects and safety planners to optimize library layout design, based on evidence-driven spatial parameter rules