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<p>Walking is a fundamental mode of urban mobility that promotes physical and mental well-being, mitigates environmental issues, and enhances community livability. As research increasingly emphasizes not only the physical but also the perceptual dimensions of walkability, understanding how pedestrians cognitively evaluate their surroundings has become a critical challenge. This study proposes a GeoAI-based framework for modeling and explaining perceived walkability in urban environments using street view imagery (SVI) and explainable AI techniques. A perceptual dataset derived from 171,118 pairwise comparisons was used to train the RSS-Swin model, enabling continuous estimation of walkability scores that reflect nuanced human judgments. Urban visual features were extracted from SVIs through semantic segmentation with SegFormer-B5, incorporating both object-level components and image brightness (HSV_V). To enhance interpretability, AutoML-based optimization and SHAP analysis were employed to identify and explain the key urban form features influencing perceived walkability. The proposed framework not only achieves high predictive accuracy but also provides transparent insights into the visual and environmental factors shaping pedestrian perception, offering practical implications for data-informed urban design and policy.</p>

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Modeling and explaining perceived walkability in urban environments using street view images and explainable AI

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月17日

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  • Jaeyeon Choi,
  • Youngok Kang

摘要

Walking is a fundamental mode of urban mobility that promotes physical and mental well-being, mitigates environmental issues, and enhances community livability. As research increasingly emphasizes not only the physical but also the perceptual dimensions of walkability, understanding how pedestrians cognitively evaluate their surroundings has become a critical challenge. This study proposes a GeoAI-based framework for modeling and explaining perceived walkability in urban environments using street view imagery (SVI) and explainable AI techniques. A perceptual dataset derived from 171,118 pairwise comparisons was used to train the RSS-Swin model, enabling continuous estimation of walkability scores that reflect nuanced human judgments. Urban visual features were extracted from SVIs through semantic segmentation with SegFormer-B5, incorporating both object-level components and image brightness (HSV_V). To enhance interpretability, AutoML-based optimization and SHAP analysis were employed to identify and explain the key urban form features influencing perceived walkability. The proposed framework not only achieves high predictive accuracy but also provides transparent insights into the visual and environmental factors shaping pedestrian perception, offering practical implications for data-informed urban design and policy.