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.

翻译：骑行适宜性评估对于推动可持续城市交通和建设自行车友好型城市至关重要，它需要纳入用户对安全性和舒适度的感知。然而，现有的基于感知的骑行适宜性评估方法在捕捉道路环境复杂性以及充分考虑主观用户感知的异质性方面面临关键局限。本文提出了一种基于人物角色感知的视觉-语言模型框架用于骑行适宜性评估，具有三项新颖贡献：(i) 基于成熟骑行者类型学的理论驱动人物角色条件化，通过思维链推理生成针对特定人物角色的解释；(ii) 多粒度监督微调，将稀缺的专家标注推理与丰富的用户评分相结合，用于联合预测与可解释评估；(iii) 人工智能驱动的数据增强，创建受控配对数据以分离基础设施变量的影响。为测试和验证该框架，我们开发了一个基于全景图像的众包系统，并从427名骑行者处收集了12,400份基于人物角色条件的评估。实验结果表明，所提出的框架在提供有竞争力的骑行适宜性评分预测的同时，独特地实现了可解释的因子归因。