Walkable to Whom? Capturing Subjective Variability in Walkability Perception Using Multimodal Deep Learning
Moloud Damandeh, Meead Saberi
cs.LG, cs.CV
Submitted: 2026-08-07
Updated: 2026-08-10
Code: https://github.com/Moloudd/user-conditionedwalkability-assessment
License: http://creativecommons.org/licenses/by-nc-sa/4.0/
The gist: Visual perception of walkability varies substantially across individuals, reflecting differences in personal characteristics, experiences, and preferences.
Terminology
Abstract
Visual perception of walkability varies substantially across individuals, reflecting differences in personal characteristics, experiences, and preferences. Existing studies, however, often reduce these diverse judgements to aggregated scores, implicitly assuming uniform perception, and commonly rely on vehicle-mounted street-view imagery that does not reflect the pedestrian's visual experience. This paper introduces a dataset of 29,870 walkability ratings from 1,196 respondents, linking sidewalk-view imagery across urban, suburban, and regional Australian environments with individual rater attributes, and proposes the first user-conditioned multimodal deep learning framework for walkability perception, fusing visual features with respondent-level representations. A viewpoint-comparison study shows that sidewalk-view images receive significantly higher walkability ratings than matched street-view images, indicating that imagery source is a substantive design decision in perception surveys. The user-conditioned model improves rank agreement with observed ratings by 65% over an image-only baseline (quadratic weighted kappa 0.47 vs. 0.29), demonstrating that who is evaluating an environment carries predictive indication beyond image content alone. These findings support moving from aggregated, observer-independent walkability scores toward models that represent diverse users, enabling more inclusive assessment of pedestrian environments.
Sources
- LANISTR: Multimodal Learning from Structured and Unstructured Data
- Bag of Tricks for Multimodal AutoML with Image, Text, and Tabular Data
- Unrestricted Permutation forces Extrapolation: Variable Importance Requires at least One More Model, or There Is No Free Variable Importance
- AutoGluon-Multimodal (AutoMM): Supercharging Multimodal AutoML with Foundation Models
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