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

• URL: http://arxiv.org/abs/2601.03534v1
• Date: Wed, 07 Jan 2026 02:46:51 GMT
• Title: Persona-aware and Explainable Bikeability Assessment: A Vision-Language Model Approach
• Authors: Yilong Dai, Ziyi Wang, Chenguang Wang, Kexin Zhou, Yiheng Qian, Susu Xu, Xiang Yan,
• Abstract summary: This paper proposes a persona-aware Vision-Language Model framework for bikeability assessment.<n>We developed a panoramic image-based crowdsourcing system and collected 12,400 persona-conditioned assessments from 427 cyclists.<n>Experiment results show that the proposed framework offers competitive bikeability rating prediction.
• Score: 8.652496663871172
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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