walkability

This paper investigates using in-context learning with the TabPFN model to predict perceived neighborhood walkability based on built environment features for mobility-impaired older adults, achieving higher performance than baseline models and employing SHAP-IQ for interaction analysis.

This paper introduces a dataset of 29,870 walkability ratings from 1,196 respondents and proposes a user-conditioned multimodal deep learning framework that fuses visual features with individual rater attributes to capture subjective variability in walkability perception. The model improves rank agreement by 65% over an image-only baseline, showing that who evaluates an environment matters.