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A large language model framework to uncover underreporting in traffic crashes.

Cristian Arteaga¹, JeeWoong Park¹

¹Department of Civil and Environmental Engineering, University of Nevada Las Vegas, USA.

Journal of Safety Research

|February 22, 2025

Summary

This study introduces a framework using Large Language Models (LLMs) to automatically identify underreported factors in traffic crash data, improving safety analysis efficiency and accuracy.

Keywords:

Alcohol involvement Crash data Large language models Traffic safety Underreporting

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Area of Science:

Traffic Safety
Data Science
Natural Language Processing

Background:

Traffic crash reports are vital for developing safety countermeasures.
Underreporting of crash factors due to data collection errors is a significant issue.
Manual data correction is time-consuming and prone to errors, especially for large datasets.

Purpose of the Study:

To develop and evaluate a framework for analyzing traffic crash narratives.
To uncover underreported crash factors using Large Language Models (LLMs).
To improve the efficiency and accuracy of traffic safety data analysis.

Main Methods:

The framework integrates prompt engineering, LLM parameter selection, output parsing, and underreporting determination.
A case study focused on identifying underreported alcohol involvement in traffic crashes.
Evaluated performance across different LLMs (ChatGPT, Flan-UL2, Llama-2), prompt types, and generation parameters using 500 Massachusetts crash reports.

Main Results:

The framework achieved high recall (up to 1.0) and precision (up to 0.93) in identifying underreported crash instances.
Demonstrated efficient and accurate uncovering of underreporting in crash data.
The approach does not require extensive natural language processing expertise from safety analysts.

Conclusions:

The developed framework effectively addresses critical gaps in traffic safety analysis.
Offers a novel method to enhance the quality and comprehensiveness of traffic crash records.
Paves the way for more effective traffic safety countermeasure development.