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

arXiv: https://arxiv.org/abs/2602.10124 · HTML: https://mlsanity.com/paper/2602.10124/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 148 references that are in the map.
- First posted on arXiv 30 Jan 2026.

## Builds on (most cited first, 13 of 13)

- [Masked-attention Mask Transformer for Universal Image Segmentation](https://mlsanity.com/paper/2112.01527/) (2021)
- [Assessing bikeability with street view imagery and computer vision](https://mlsanity.com/paper/2105.08499/) (2021)
- [Global urban visual perception varies across demographics and personalities](https://mlsanity.com/paper/2505.12758/) (2025)
- [A citizen science toolkit to collect human perceptions of urban environments using open street view images](https://mlsanity.com/paper/2403.00174/) (2024)
- [Urban Safety Perception Assessments via Integrating Multimodal Large Language Models with Street View Images](https://mlsanity.com/paper/2407.19719/) (2024)
- [TravelAgent: Generative Agents in the Built Environment](https://mlsanity.com/paper/2412.18985/) (2024)
- [StreetReaderAI: Making Street View Accessible Using Context-Aware Multimodal AI](https://mlsanity.com/paper/2508.08524/) (2025)
- [Urban Bike Lane Planning with Bike Trajectories: Models, Algorithms, and a Real-World Case Study](https://mlsanity.com/paper/2008.09645/) (2020)
- [Revealing the determinants of gender inequality in urban cycling with large-scale data](https://mlsanity.com/paper/2203.09378/) (2022)
- [Multiple Object Detection and Tracking in Panoramic Videos for Cycling Safety Analysis](https://mlsanity.com/paper/2407.15199/) (2024)
- [A Context Aware and Video-Based Risk Descriptor for Cyclists](https://mlsanity.com/paper/1704.07490/) (2017)
- [Cyclist Trajectory Forecasts by Incorporation of Multi-View Video Information](https://mlsanity.com/paper/2106.15991/) (2021)
- [How built environment shapes cycling experience: A multi-scale review in historical urban contexts](https://mlsanity.com/paper/2509.15657/) (2025)

## Abstract (arXiv)

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.