Smart AI-Technology Employment for Crash Data Analysis

In 2024, motor vehicle traffic crashes in the United States resulted in 39,345 fatalities and approximately 2.44 million injuries. Drug-involved driving has emerged as a significant and growing contributor to these crashes, yet unlike alcohol impairment, drug involvement remains difficult to detect due to the diversity of substances and their varying effects on driver behavior. Research indicates that more than 20% of drug-related crashes are erroneously recorded, compromising data accuracy and undermining effective policy responses. While most traffic safety studies rely on statistical approaches and quantitative data to identify crash severity contributors, these methods often fail to capture the nuanced circumstances surrounding drug-involved incidents. Manual review of crash narratives is inefficient and error-prone, and traditional keyword-based searches frequently miss contextual information, producing false positives and negatives. Although previous studies have demonstrated that crash narratives can reveal contributing factors difficult to obtain through conventional quantitative analysis, these efforts were limited to classic text mining techniques that consider only individual word frequencies without capturing language semantics. This research leverages advances in Artificial Intelligence and Natural Language Processing to systematically analyze crash narrative data, a historically underutilized resource to extract new insights about drug-involved vehicle crashes. The study aims to provide foundational evidence for addressing drug-involved crashes and improving traffic safety management while advancing methodological approaches for narrative-based crash analysis. Key findings and methodological innovations will be integrated into educational and outreach initiatives.

Language

• English

Project

• Status: Active
• Funding: $95,295.00
• Contract Numbers:

  69A3552348323

• 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:

  Howard University

  2400 6th Street, NW
  Washington, DC  United States  20059
• Project Managers:

  Bruner, Britain

• Performing Organizations:

  University of Nevada, Las Vegas

  Las Vegas, NV  United States 
• Principal Investigators:

  Park, Jee

• Start Date: 20260102
• Expected Completion Date: 20260930
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers Program

Subject/Index Terms

• TRT Terms: Artificial intelligence; Crash data; Data analysis; Drugged drivers
• Subject Areas: Data and Information Technology; Highways; Safety and Human Factors;

Filing Info

• Accession Number: 01976919
• Record Type: Research project
• Source Agency: Research and Education for Promoting Safety (REPS) University Transportation Center
• Contract Numbers: 69A3552348323
• Files: UTC, RIP
• Created Date: Jan 26 2026 12:27AM