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

Yilong Dai; Ziyi Wang; Chenguang Wang; Kexin Zhou; Yiheng Qian; Susu Xu; Xiang Yan

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arXiv · 2601.03534

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

Yilong Dai·Ziyi Wang·Chenguang Wang·Kexin Zhou·Yiheng Qian·Susu Xu·Xiang Yan

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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.

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Yilong Dai, Ziyi Wang, Chenguang Wang, Kexin Zhou, Yiheng Qian, Susu Xu, Xiang Yan. 2026-01-07. Persona-aware and Explainable Bikeability Assessment: A Vision-Language Model Approach. https://arxiv.org/abs/2601.03534

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