Document Type
Poster
Journal/Book Title/Conference
National Conference of Undergraduate Research
Location
Pittsburgh, PA
Publication Date
4-9-2025
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Abstract
Background: Intersections serve as vital nodes within transportation networks, facilitating the movement of pedestrians. However, their effectiveness often hinges on the quality of their infrastructure and accessibility. Furthermore, it is expensive and time-consuming to collect infrastructure data, yet is crucial for creating a safe and efficient urban landscape.
Objective: Develop an AI-driven model that leverages Google Street View to provide detailed information about intersections, helping city planners identify infrastructure and accessibility issues and supporting improvements to create safer, more accessible intersections for people of all abilities.
Recommended Citation
Liddiard, Logan; Pierson, Joel; Chamberlain, Brent; Christensen, Keith; and Qi, Xiaojun, "An AI-Driven Framework for Assessing Intersection Infrastructure and Accessibility" (2025). Computer Science Student Research. Paper 51.
https://digitalcommons.usu.edu/computer_science_stures/51
