Large Language Model-Driven Crash Risk Analysis System for Rural and Tribal Roadways

Rural and tribal roadways in the United States experience disproportionately high crash incidents due to a combination of infrastructure challenges, limited resources, and incomplete crash reporting. Traditional statistical, machine learning (ML), and deep learning (DL) models have struggled to address these issues because they rely heavily on structured data, perform poorly with incomplete or imbalanced datasets, and often fail to leverage the rich information contained in crash narratives. Large language models (LLMs) offer an alternative by reframing crash risk analysis as a text reasoning problem, enabling the extraction of contextual insights from narratives, the imputation of missing or inconsistent fields, and the integration of structured and unstructured data into unified predictive frameworks. This proposal aims to develop an LLM-based crash risk analysis system utilizing North Carolina’s statewide police-reported crash data as a foundation, with the goal of enhancing the accuracy, interpretability, and robustness of rural crash risk assessment. The project will proceed through four main tasks: crash data enhancement and model adaptation, predictive model development, model validation, and the design of an implementation plan for real-time crash risk warnings in connected vehicle (CV) environments.

• Record URL:
  • https://www.cr2c2.com/research/major-research-initiatives/mri-2/project-r2-6

Language

• English

Project

• Status: Active
• Funding: $150,035.00
• Contract Numbers:

  69A3552348304

• 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:

  North Carolina A&T State University

  1601 E. Market Street
  Greensboro, NC  United States  27411
• Project Managers:

  Comert, Gurcan

• Performing Organizations:

  North Carolina A&T State University

  1601 E. Market Street
  Greensboro, NC  United States  27411

  Clemson University

  216 Lowry Hall
  Clemson, SC, SC  United States  29634
• Principal Investigators:

  Comert, Gurcan

  Chowdhury, Mashrur

  Salek, M

• Start Date: 20260815
• Expected Completion Date: 20270814
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers Program
• Subprogram: Center for Transportation Research

Subject/Index Terms

• TRT Terms: Crash risk forecasting; Data analysis; Deep learning; Police reports; Predictive models; Rural areas
• Geographic Terms: North Carolina
• Subject Areas: Data and Information Technology; Highways; Planning and Forecasting; Safety and Human Factors;

Filing Info

• Accession Number: 01995871
• Record Type: Research project
• Source Agency: Center for Regional and Rural Connected Communities
• Contract Numbers: 69A3552348304
• Files: UTC, RIP
• Created Date: Jul 17 2026 4:08PM