Persona-aware and Explainable Bikeability Assessment: A Vision-Language Model Approach

TL;DR

This paper introduces a novel persona-aware vision-language model for bikeability assessment that incorporates cyclist typologies, combines expert and user data for explainability, and uses data augmentation to analyze infrastructure impacts.

Contribution

It presents a new framework integrating persona conditioning, multi-granularity fine-tuning, and AI data augmentation for explainable bikeability assessment.

Findings

Achieved competitive bikeability rating predictions.

Enabled explainable factor attribution.

Collected 12,400 assessments from 427 cyclists.

Abstract

Bikeability assessment is essential for advancing sustainable urban transportation and creating cyclist-friendly cities, and it requires incorporating users' perceptions of safety and comfort. Yet existing perception-based bikeability assessment approaches face key limitations in capturing the complexity of road environments and adequately accounting for heterogeneity in subjective user perceptions. This paper proposes a persona-aware Vision-Language Model framework for bikeability assessment with three novel contributions: (i) theory-grounded persona conditioning based on established cyclist typology that generates persona-specific explanations via chain-of-thought reasoning; (ii) multi-granularity supervised fine-tuning that combines scarce expert-annotated reasoning with abundant user ratings for joint prediction and explainable assessment; and (iii) AI-enabled data augmentation that creates controlled paired data to isolate infrastructure variable impacts. To test and validate this framework, we developed a panoramic image-based crowdsourcing system and collected 12,400 persona-conditioned assessments from 427 cyclists. Experiment results show that the proposed framework offers competitive bikeability rating prediction while uniquely enabling explainable factor attribution.