A CPTED-Guided Interpretable Perception Network for Assessing Perceived Safety Along Urban Greenway Walking Boundaries

Perceived safety determines whether urban greenways are used in everyday life, yet it is rarely measurable at the boundary scale where design decisions are made. Existing street-view models split into black-box networks whose predictions cannot be traced to design elements and pixel-ratio regressions whose interpretability rests on weak, unstructured representations, while greenspace studies lean on GIS proximity variables that confound design with context. We present the CPTED-Guided Perception Network (CGPN), which fuses a visual branch with a masked, learnable projection of segmentation ratios onto five CPTED dimensions. Because the mask confines learning to a theory-defined support, the prior regularizes the representation while every coordinate of the model remains tied to a named CPTED dimension, whose directional effect on the prediction we verify by perturbation. On 110,633 street-view images, CGPN is statistically equivalent to the strongest black-box baseline in pairwise ranking accuracy (0.649 vs. 0.652; equivalence test within a 1.5-point margin, p=0.006, attains the best R2 (0.192), and improves on its unconstrained variant in goodness of fit across three seeds (ΔR2=+0.031, p=0.042). Applied to 218 greenway-adjacent residential boundaries in Boston and New York, it uncovers a threshold-like negative association for barrier-dominated access control and an inverted-U distance profile whose weakest segment lies within 100 m of the greenway edge (p=0.007).