Sidewalk sheds are a common feature of the streetscape in New York City, reflecting ongoing construction and maintenance activities. However, policymakers and local business owners have raised concerns about reduced storefront visibility and altered pedestrian navigation. Although sidewalk sheds are widely used for safety, their effects on pedestrian visibility and movement are not directly measured in current planning practices. To address this, we developed an AI-based chatbot survey that collects image-based annotations and route choices from pedestrians, linking these responses to specific shed design features, including clearance height, post spacing, and color. This AI chatbot survey integrates a large language model (e.g., Google's Gemini-1.5-flash-001 model) with an image-annotation interface, allowing users to interact with street images, mark visual elements, and provide structured feedback through guided dialogue. To explore pedestrian perceptions and behaviors, this paper conducts a grid-based analysis of entrance annotations and applies logistic mixed-effects modeling to assess sidewalk choice patterns. Analysis of the dataset (n = 25) shows that: (1) the presence of scaffolding significantly reduces pedestrians' ability to identify ground-floor retail entrances, and (2) variations in weather conditions and shed design features significantly influence sidewalk selection behavior. By integrating generative AI into urban research, this study demonstrates a novel method for evaluating sidewalk shed designs and provides empirical evidence to support adjustments to shed guidelines that improve the pedestrian experience without compromising safety.

翻译：人行道棚屋是纽约市街道景观的常见特征，反映了持续的施工与维护活动。然而，政策制定者与本地商户对其导致的店面可见度降低及行人导航路径改变表示担忧。尽管人行道棚屋被广泛用于安全保障，但其对行人视线与移动的影响在当前规划实践中并未得到直接测量。为此，我们开发了一种基于人工智能的聊天机器人调查工具，用于收集行人基于图像的标注信息与路径选择数据，并将这些反馈与棚屋的具体设计特征（包括净空高度、立柱间距与颜色）相关联。该AI聊天机器人调查将大型语言模型（例如Google的Gemini-1.5-flash-001模型）与图像标注界面相结合，使用户能够与街景图像交互、标记视觉元素，并通过引导式对话提供结构化反馈。为探究行人的感知与行为，本文对入口标注进行了网格化分析，并应用逻辑混合效应模型评估人行道选择模式。对数据集（n = 25）的分析表明：（1）脚手架的存在显著降低了行人识别底层零售入口的能力；（2）天气条件与棚屋设计特征的变化显著影响人行道选择行为。通过将生成式人工智能融入城市研究，本研究展示了一种评估人行道棚屋设计的新方法，并为调整棚屋规范提供了实证依据，旨在提升行人体验的同时不牺牲安全性。