用AI识别交警记录中误分类的交通事故文本，提升数据准确性。
Identification of Potentially Misclassified Crash Narratives using Machine Learning (ML) and Deep Learning (DL)
- 融合文本与结构化数据的混合模型检测误分类事故
- Albert模型与专家判断一致率达73%，错误率降低54.2%
- 适合交通安全管理与政策制定者提升数据质量
本研究探讨机器学习（ML）与深度学习（DL）方法在识别警察报告中误分类的交叉口事故叙述方面的有效性。基于爱荷华州交通部2019年事故数据，我们实现了并比较了包括支持向量机（SVM）、XGBoost、BERT句向量、BERT词向量及Albert模型在内的多种模型。模型性能通过专家对潜在误分类叙述的评审进行系统验证，评估分类准确率。结果表明，尽管传统ML方法整体表现优于部分DL方法，但Albert模型与专家分类的一致性最高（专家1为73%），与原始表格数据的一致性为58%。统计分析显示，Albert模型的表现接近专家间一致性水平，尤其在模糊叙述上显著优于其他方法。本研究通过多模态整合分析填补了交通安全管理中的关键空白，结合叙述文本与结构化数据使错误率减少54.2%。结论指出，将自动化分类与针对性专家审查结合，是提升事故数据质量的实用方法，对交通安全管理与政策制定具有重要意义。
原文摘要 · Abstract (English)
This research investigates the efficacy of machine learning (ML) and deep learning (DL) methods in detecting misclassified intersection-related crashes in police-reported narratives. Using 2019 crash data from the Iowa Department of Transportation, we implemented and compared a comprehensive set of models, including Support Vector Machine (SVM), XGBoost, BERT Sentence Embeddings, BERT Word Embeddings, and Albert Model. Model performance was systematically validated against expert reviews of potentially misclassified narratives, providing a rigorous assessment of classification accuracy. Results demonstrated that while traditional ML methods exhibited superior overall performance compared to some DL approaches, the Albert Model achieved the highest agreement with expert classifications (73% with Expert 1) and original tabular data (58%). Statistical analysis revealed that the Albert Model maintained performance levels similar to inter-expert consistency rates, significantly outperforming other approaches, particularly on ambiguous narratives. This work addresses a critical gap in transportation safety research through multi-modal integration analysis, which achieved a 54.2% reduction in error rates by combining narrative text with structured crash data. We conclude that hybrid approaches combining automated classification with targeted expert review offer a practical methodology for improving crash data quality, with substantial implications for transportation safety management and policy development.
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