This research explores the impact of urban design performance qualities on pedestrian behavior in a cultural heritage site designated by UNESCO. The study employs a multi-method approach, including a questionnaire survey, empirical observation of pedestrian activities, and empirical axial line and visibility graph analysis using the space syntax technique. The first part of the study involved a questionnaire formatted as a polling sheet to gather expert assessments of spatial performance measures. The second part used a pilot survey to capture the perspectives of end users regarding the study’s objectives and their perceptions of the site. Pedestrian flow was observed using a technique called “gate counts”, with observations recorded as video clips during specific morning and afternoon periods across three pedestrian zones. The study also examined the behavioral patterns of pedestrians, including their movement patterns. Finally, the ArcGIS 10.3.1 software was employed to evaluate the reliability of the results. The main finding of this research is that pedestrian behavior and walkability in the historical areas are significantly influenced by landmark integration, wayfinding behavior, and the socio-economic functions of heritage sites. This study highlights the importance of using cognitive and syntactic analysis, community engagement, and historical preservation to enhance walkability, accessibility, and social interaction in heritage contexts. In addition, it identifies the need for improvements in urban design to address inconsistencies between syntactic maps and actual pedestrian flow, emphasizing the role of imageability and the impact of environmental and aesthetic factors on pedestrian movement. This research provides valuable insights for urban designers and planners, environmental psychologists, architects, and policymakers by highlighting the key elements that make urban spaces walkable, aiming to enhance the quality of public spaces.

This study examines pedestrian perceptions of streetscapes in Isfahan’s cultural heritage site by integrating deep learning–based image segmentation with urban morphological analysis. It addresses the opportunity to develop a scalable and context-sensitive method for assessing pedestrian-oriented heritage streetscapes, particularly where conventional street-view datasets are unavailable. Using a U-Net model applied to First-Person Pedestrian View (FPPV) images, five perceptual indices, imageability, enclosure, human scale, greenness, and walking index, were quantified to examine their associations with pedestrian experience. Street width was incorporated as a morphological variable to explore its relationship with perceptual qualities using Spearman correlation and visual trend analysis. The results indicate exploratory associations between visual composition and perceptual outcomes within the analysed heritage streetscape context, particularly between imageability, enclosure, and vegetation structure. In contrast, variables such as human scale and walking index showed weak or negligible associations with street width, indicating that pedestrian activity patterns within the analysed heritage streetscape may be influenced by additional spatial, landscape, and socio-functional factors beyond dimensional characteristics alone. Segmentation-based analysis achieved an accuracy of 83% in classifying dominant streetscape elements, offering a reproducible alternative to traditional survey-based methods. This study contributes a data-driven framework for assessing pedestrian streetscapes, emphasising morphological continuity, human-scale design, and green infrastructure as important dimensions of walkability assessment. It also identifies key challenges, including fragmented spatial morphology and inconsistent urban furniture placement, which may affect pedestrian comfort and use of space. These findings offer evidence-informed design considerations for historic streetscape assessment, with implications for balancing heritage conservation and contemporary pedestrian needs. Future research may refine perceptual metrics, incorporate behavioural or longitudinal validation, and extend the approach across diverse urban contexts.

This study examines pedestrian perceptions of streetscapes in Isfahan’s cultural heritage site by integrating deep learning–based image segmentation with urban morphological analysis. Using a U-Net model applied to First-Person Pedestrian View (FPPV) images, five perceptual indices (imageability, enclosure, human scale, greenness, and walking index) were quantified to assess their influence on pedestrian experience. Street width was explicitly incorporated as a morphological variable to examine its relationship with perceptual qualities using spearman correlation analysis and visual trend analysis using Pearson correlation. The results reveal consistent relationships between visual composition and perceptual outcomes, particularly strong associations between imageability, enclosure, and vegetation structure, as well as trade-offs between enclosure and sky visibility. In contrast, variables such as human scale and walking index show weak or negligible associations with street width, suggesting that pedestrian presence and activity patterns in heritage contexts are more strongly influenced by landscape elements, water features, and spatial continuity than by dimensional factors alone. Findings highlight how urban renewal strategies, such as streetscape enhancement and cultural preservation, shape pedestrian movement and spatial perception. Segmentation-based analysis achieved an accuracy of 83% in classifying dominant streetscape elements, offering a robust alternative to traditional survey-based methods. This study contributes a data-driven framework for assessing pedestrian streetscapes, emphasizing morphological continuity, human-scale design, and green infrastructure as critical determinants of walkability. It also identifies key challenges, including fragmented spatial morphology and inconsistent urban furniture placement, which affect pedestrian comfort and use of space. These findings support evidence-based policy and design strategies for optimizing historic urban streetscapes, with implications for balancing heritage conservation and modern pedestrian needs. Future research may refine perceptual metrics and extend the approach across diverse urban contexts.