Exploring Sidewalk Sheds in New York City through Chatbot Surveys and Human Computer Interaction

TL;DR

This study introduces an AI chatbot survey combining image annotation and dialogue to evaluate how sidewalk shed designs impact pedestrian visibility and movement in New York City.

Contribution

It presents a novel AI-based survey method integrating large language models and image annotation to assess sidewalk shed effects on pedestrians.

Findings

Scaffolding reduces pedestrians' ability to identify retail entrances.

Weather and shed design features influence sidewalk choice behavior.

AI integration offers a new approach for urban infrastructure evaluation.

Abstract

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.