Exploring Sidewalk Sheds in New York City through Chatbot Surveys and Human Computer Interaction
This addresses concerns of policymakers and business owners about sidewalk sheds' impacts on storefront visibility and pedestrian navigation, though it appears incremental in applying existing AI methods to urban research.
This study tackled the problem of measuring how sidewalk sheds affect pedestrian visibility and movement in New York City by developing an AI chatbot survey that collects image-based annotations and route choices from pedestrians. The results showed that scaffolding significantly reduces pedestrians' ability to identify retail entrances and that weather and shed design features influence sidewalk selection behavior.
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.