URBAN-SPIN: A street-level bikeability index to inform design implementations in historical city centres

TL;DR

This paper introduces URBAN-SPIN, a bikeability index tailored for historical city centers that combines visual, spatial, and subjective data to evaluate and improve cycling experiences without large infrastructural changes.

Contribution

It develops a perception-led, typology-based framework integrating computer vision, subjective ratings, and physical metrics to assess street-level bikeability in constrained urban environments.

Findings

Greenness and openness improve perceived bikeability.

Enclosure and imageability have variable effects depending on street type.

Targeted visual redesigns can enhance cycling comfort and pleasure.

Abstract

Cycling is reported by an average of 35\% of adults at least once per week across 28 countries, and as vulnerable road users directly exposed to their surroundings, cyclists experience the street at an intensity unmatched by other modes. Yet the street-level features that shape this experience remain under-analysed, particularly in historical urban contexts where spatial constraints rule out large-scale infrastructural change and where typological context is often overlooked. This study develops a perception-led, typology-based, and data-integrated framework that explicitly models street typologies and their sub-classifications to evaluate how visual and spatial configurations shape cycling experience. Drawing on the Cambridge Cycling Experience Video Dataset (CCEVD), a first-person and handlebar-mounted corpus developed in this study, we extract fine-grained streetscape indicators with computer vision and pair them with built-environment variables and subjective ratings from a Balanced Incomplete Block Design (BIBD) survey, thereby constructing a typology-sensitive Bikeability Index that integrates subjective and perceived dimensions with physical metrics for segment-level comparison. Statistical analysis shows that perceived bikeability arises from cumulative, context-specific interactions among features. While greenness and openness consistently enhance comfort and pleasure, enclosure, imageability, and building continuity display threshold or divergent effects contingent on street type and subtype. AI-assisted visual redesigns further demonstrate that subtle, targeted changes can yield meaningful perceptual gains without large-scale structural interventions. The framework offers a transferable model for evaluating and improving cycling conditions in heritage cities through perceptually attuned, typology-aware design strategies.