Scholarlink

<p>This study presents a data-driven framework to uncover patterns of less-studied fatal driver behaviors. We analyzed 84,170 driver behavior reports from the Fatality Analysis Reporting System (FARS) between 2021 and 2023. Using natural language processing methods, we obtained 194 distinct lemmas and quantified their importance in each report. We then applied principal components analysis to obtain 18 underlying behavioral factors. Finally, we clustered the reports via Gaussian Mixture Modeling, obtaining a novel typology of 15 behaviors. Our results reveal spatial disparities in fatal driver behaviors and provide a framework for type-specific crash mitigation efforts.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Novel Typology of Fatal Crash Behaviors via Natural Language Processing

  • 期刊论文

  • 发表日期：

    2025年10月23日

查看全文
  • Zhuo Han,
  • Jimi Oke

摘要

This study presents a data-driven framework to uncover patterns of less-studied fatal driver behaviors. We analyzed 84,170 driver behavior reports from the Fatality Analysis Reporting System (FARS) between 2021 and 2023. Using natural language processing methods, we obtained 194 distinct lemmas and quantified their importance in each report. We then applied principal components analysis to obtain 18 underlying behavioral factors. Finally, we clustered the reports via Gaussian Mixture Modeling, obtaining a novel typology of 15 behaviors. Our results reveal spatial disparities in fatal driver behaviors and provide a framework for type-specific crash mitigation efforts.