What question did this study set out to answer?

The aim is to develop a framework for assessing pedestrian comfort by integrating subjective experiences with urban characteristics in high-density cities.

March 13, 2026Open Access

A Machine-Learning Approach for Evaluating Perceived Walking Comfort in Macau’s High-Density Urban Environment

Puntos clave

The aim is to develop a framework for assessing pedestrian comfort by integrating subjective experiences with urban characteristics in high-density cities.
Developed an integrated framework using pairwise comparison scoring and semantic segmentation.
Employed ensemble learning with Random Forest and EfficientNet-B7 to derive continuous comfort scores.
Conducted SHAP analysis for interpretability and to understand the relationships between comfort scores and spatial configurations.
Classified Macau’s street network into four experiential street types.
Identified stable associations between predicted comfort scores and various urban characteristics.
Found that cultural legibility and moderate greenery enhance comfort in specific street types.
Demonstrated that machine learning effectively models perceived pedestrian comfort in high-density environments.

Resumen

Evaluating pedestrian comfort in high-density cities requires methods integrating subjective experience with urban morphology. This study develops an integrated framework combining pairwise comparison scoring, semantic segmentation (DeepLabv3+), ensemble learning (Random Forest), and SHAP-based interpretability. EfficientNet-B7 is used to expand pairwise datasets and derive continuous comfort scores across Macau’s street network. Four experiential street types are identified: historical–cultural districts, urban lifestyle areas, natural corridors, and leisure zones. SHAP analysis illustrates stable associations between predicted comfort scores and multi-layered spatial configurations, including cultural legibility and sequencing in historic cores, moderate greenery with functional anchoring in residential areas, and scene coherence in tourism zones. Semantic features serve as effective morphological proxies within the modeling framework. Methodologically, the framework demonstrates how explainable machine learning can be applied to dense Asian cities under observational conditions. Design implications emphasize interface continuity, microclimate adaptation, and functional enrichment, suggesting that pedestrian comfort is closely related to coherent spatial–experiential structures rather than isolated environmental upgrades.

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