用视觉语言模型评估骑行友好度，还能解释不同人群的偏好差异。
Persona-aware and Explainable Bikeability Assessment: A Vision-Language Model Approach
- 基于骑行者类型构建个性化的解释生成机制
- 在12,400份评估数据上实现精准评分与可解释分析
- 适合城市规划与交通设计人员参考使用
骑行友好度评估对推动可持续城市交通、打造骑行友好型城市至关重要，需融合用户对安全与舒适性的主观感知。然而现有基于感知的评估方法难以捕捉道路环境复杂性，且无法充分反映用户感知的异质性。本文提出一种面向人物画像的视觉语言模型框架，具三项创新：(i) 基于成熟骑行者类型理论的人物画像条件化，通过思维链推理生成个性化解释；(ii) 多粒度监督微调，结合稀缺专家标注推理与海量用户评分，实现联合预测与可解释评估；(iii) AI驱动的数据增强，生成受控配对数据以分离基础设施变量影响。为验证框架，我们构建了全景图像众包系统，收集了来自427名骑行者的12,400份人物画像条件化评估。实验表明，该框架在骑行友好度评分预测上表现优异，且首次实现可解释的因素归因。
原文摘要 · Abstract (English)
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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