Measuring streetscapes’ threshold intervals on pedestrian perception: An evidence-based study integrating virtual reality and wearable biosensors

The growing emphasis on walkable environments underscores the need to understand how streetscapes influence pedestrian perception, yet the specific threshold intervals at which these effects become significant remain underexplored. This study proposes a workable framework that integrates immersive VR, wearable biosensors, and machine-learning algorithms to identify refined thresholds and further advance evidence-based urban design. Initial steps involved identifying key streetscape elements across different street types, for example, sidewalk width, interface permeability, utility area width, and cycle parking, as informed by literature and existing guidelines. This was followed by constructing 251 VR streetscape scenes, through which subjective evaluations were collected from 185 participants to establish streetscapes’ preliminary thresholds. Subsequently, wearable biosensors and a window-based change point detection algorithm were employed to determine refined threshold intervals, the results of which informed actionable guidelines for street design. The framework integrates human-centered analytical tools to systematically quantify refined streetscapes’ thresholds and translates them into evidence-based guidelines for urban design, offering a replicable pathway for optimizing streetscape quality. Overall, the study provides quantitative insights and actionable design guidance that advance the broader agenda of evidence-based and human-centered urban design.