This project aims to leverage Large Language Models (LLMs) to enhance the analysis of public complaints and suggestions related to transportation systems. By processing feedback from multiple agencies, this study seeks to cluster and analyze common concerns, aiding agencies in aligning their services with public demands and safety needs. The project identifies complaint patterns, to inform responsive infrastructure policies. An open-source LLM model will be developed to safeguard privacy while enabling data-driven improvements in transportation services.
Universities Involved
University of Pittsburgh
Principal Investigators
Lev Khazanovich
Aleksandar Stevanovic
Expected Research Outcomes & Impacts
The project is expected to deliver an open-source LLM capable of analyzing and categorizing public transportation feedback, enabling proactive responses to recurring issues. By addressing public needs with actionable data, findings will facilitate enhanced service quality and effective resource allocation for transportation agencies.
Subject Areas
Artificial Intelligence, Large Language Models, Public Engagement