安全交通副试点:适应大型语言模型,以进行可靠的交通安全评估和决策干预
Yang Zhao1,2, Pu Wang1,2, Yibo Zhao1
1Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD, USA.
Nature communications
|October 7, 2025
概括
SafeTraffic Copilot使用大型语言模型 (LLM) 预测交通事故并识别关键风险因素,提高预测准确性并指导针对性的安全干预,以提高道路安全.
科学领域:
- 人工智能的人工智能
- 运输安全运输安全
- 数据科学数据科学数据科学
背景情况:
- 交通事故预测是复杂的,缺乏可靠的模型.
- 现有的方法与事故数据固有的复杂性作斗争.
研究的目的:
- 介绍SafeTraffic Copilot,这是一个基于LLM的系统,用于交通事故预测和干预.
- 提高交通安全分析中的预测准确性和可靠性.
- 为有针对性的安全干预提供可解释的见解.
主要方法:
- 适应大型语言模型 (LLM) 用于交通事故预测作为文本推理任务.
- 在SafeTraffic事件数据集上微调定制的LLM (66,205个真实世界的崩案例).
- 开发了SafeTraffic Attribution,用于句子级特征赋值和"如果"风险分析.
主要成果:
- 在预测任务中,SafeTraffic LLM实现了33.3%至45.8%更高的平均F1分数.
- 鉴定出酒后驾驶和攻击性行为是严重撞车的主要因素.
- 安全交通归因指导数据收集用于不断改进模型.
结论:
- 在崩预测中,SafeTraffic Copilot提供了改进的概括性,适应性和可靠性.
- 该系统能够预测和推理有条件撞车风险.
- 结果支持通过数据驱动的洞察力改善交通安全.
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