一个大型的语言模型框架,以揭示交通事故中报告不足的情况
Cristian Arteaga1, JeeWoong Park1
1Department of Civil and Environmental Engineering, University of Nevada Las Vegas, USA.
Journal of safety research
|February 22, 2025
概括
本研究介绍了一种使用大型语言模型 (LLM) 的框架,用于自动识别交通事故数据中报告不足的因素,提高安全分析的效率和准确性.
科学领域:
- 交通安全 交通安全
- 数据科学数据科学数据科学
- 自然语言处理自然语言处理.
背景情况:
- 交通事故报告对于制定安全对策至关重要.
- 由于数据收集错误导致的崩因素报告不足是一个重大问题.
- 手动数据校正是耗时且容易出现错误的,特别是对于大型数据集.
研究的目的:
- 开发和评估一个分析交通事故叙述的框架.
- 使用大型语言模型 (LLM) 发现未被报告的崩因素.
- 提高交通安全数据分析的效率和准确性.
主要方法:
- 该框架集成了提示工程,LLM参数选择,输出解析和不足报告的确定.
- 一个案例研究的重点是确定交通事故中未被报告的酒精参与.
- 通过500份马萨诸塞州的崩报告评估了不同LLM (ChatGPT,Flan-UL2,Llama-2),提示类型和生成参数的性能.
主要成果:
- 该框架实现了高回忆率 (高达1.0) 和精度 (高达0.93) 在识别报告不足的崩实例.
- 证明有效和准确地揭示了事故数据中报告不足的情况.
- 这种方法不需要安全分析师广泛的自然语言处理专业知识.
结论:
- 开发的框架有效地解决了交通安全分析中的关键差距.
- 提供了一种新的方法来提高交通事故记录的质量和全面性.
- 为更有效的交通安全反制措施开发铺平了道路.
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