用大模型分析高速公路事故成因，识别关键因素。
Advanced Crash Causation Analysis for Freeway Safety: A Large Language Model Approach to Identifying Key Contributing Factors
- 基于226篇文献构建数据集，微调Llama3 8B模型理解事故成因。
- 零样本分类准确识别酒驾、超速等主要致因，效果获专家认可。
- 适合交通规划与政策制定者，助力提升道路安全措施。
理解交通事故成因并制定缓解策略至关重要。传统统计方法与机器学习模型难以捕捉各类因素间的复杂交互及每起事故的独特特征。本研究利用大语言模型（LLM）分析高速公路事故数据，实现事故成因分析。通过整合226篇相关交通安全隐患研究，构建涵盖环境、驾驶员、交通流与道路几何设计因素的训练数据集。采用QLoRA对Llama3 8B模型进行微调，增强其对事故成因的理解。随后，该模型在无预标注数据情况下通过零样本分类识别事故原因，提供全面解释，确保结果合理且与现有研究一致。结果显示，模型能有效识别酒驾、超速、攻击性驾驶和注意力不集中等主要致因；结合道路养护事件等动态数据可获得更深层洞察。通过问卷调查验证，领域专家对模型结果的认可度达88.89%，表明其具备实际应用价值。研究揭示了交通事故的复杂性，展示了LLM在事故成因综合分析中的潜力，并为规划者与决策者提供可行对策，推动更高效、精准的交通安全实践。
原文摘要 · Abstract (English)
Understanding the factors contributing to traffic crashes and developing strategies to mitigate their severity is essential. Traditional statistical methods and machine learning models often struggle to capture the complex interactions between various factors and the unique characteristics of each crash. This research leverages large language model (LLM) to analyze freeway crash data and provide crash causation analysis accordingly. By compiling 226 traffic safety studies related to freeway crashes, a training dataset encompassing environmental, driver, traffic, and geometric design factors was created. The Llama3 8B model was fine-tuned using QLoRA to enhance its understanding of freeway crashes and their contributing factors, as covered in these studies. The fine-tuned Llama3 8B model was then used to identify crash causation without pre-labeled data through zero-shot classification, providing comprehensive explanations to ensure that the identified causes were reasonable and aligned with existing research. Results demonstrate that LLMs effectively identify primary crash causes such as alcohol-impaired driving, speeding, aggressive driving, and driver inattention. Incorporating event data, such as road maintenance, offers more profound insights. The model's practical applicability and potential to improve traffic safety measures were validated by a high level of agreement among researchers in the field of traffic safety, as reflected in questionnaire results with 88.89%. This research highlights the complex nature of traffic crashes and how LLMs can be used for comprehensive analysis of crash causation and other contributing factors. Moreover, it provides valuable insights and potential countermeasures to aid planners and policymakers in developing more effective and efficient traffic safety practices.
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