Incorporating Large Language Models (LLMs) into Transportation Safety Analytics and Equity

This project focuses on enhancing transportation safety and equity through Large Language Models (LLMs). It aims to improve crash data analysis, highlighting disparities in traffic safety, especially for vulnerable road users and underserved communities. Utilizing the Connecticut Crash Data Repository, the project will develop an LLM algorithm to automatically analyze crash patterns and generate visual reports. This innovative approach is designed to identify and address inequities in transportation safety, aligning with the new Bipartisan Infrastructure Law's requirements. The interdisciplinary team brings together expertise in machine learning and transportation safety, ensuring a comprehensive approach to tackling these challenges. The project's outcomes are expected to influence policy and planning decisions, leading to safer and more equitable transportation systems.

Language

• English

Project

• Status: Active
• Funding: $106000
• Contract Numbers:

  69A3552348301

• Sponsor Organizations:

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Managing Organizations:

  University of Massachusetts, Amherst

  Department of Civil and Environmental Engineering
  130 Natural Resources Road
  Amherst, MA  United States  01003
• Performing Organizations:

  University of Connecticut, Storrs

  Connecticut Transportation Institute
  270 Middle Turnpike, Unit 5202
  Storrs, CT  United States  06269-5202
• Principal Investigators:

  Ding, Caiwen

  Wang, Kai

• Start Date: 20240101
• Expected Completion Date: 20241231
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers Program
• Subprogram: University Transportation Centers

Subject/Index Terms

• TRT Terms: Algorithms; Crash data; Data analysis; Equity; Language; Transportation safety
• Geographic Terms: Connecticut
• Subject Areas: Planning and Forecasting; Safety and Human Factors; Transportation (General);

Filing Info

• Accession Number: 01904458
• Record Type: Research project
• Source Agency: New England University Transportation Center
• Contract Numbers: 69A3552348301
• Files: UTC, RIP
• Created Date: Jan 12 2024 10:46AM