用深度学习分析儿童骑行事故严重程度，提升预测准确率。
Crash Severity Analysis of Child Bicyclists using Arm-Net and MambaNet
- 采用ARM-Net与MambaNet处理表格数据，结合SMOTEENN平衡样本。
- MambaNet在致命/严重与无伤类事故上表现更优，准确率更高。
- 适合交通安全研究者与智能交通系统开发者参考。
14岁及以下儿童骑行者是道路中最脆弱的群体之一，常在事故中遭受严重伤害或死亡。本研究基于2017至2022年德克萨斯州2,394起儿童骑行事故数据，采用两种深度表格式学习模型（ARM-Net与MambaNet）进行事故严重程度分析。为解决数据不平衡问题，使用SMOTEENN技术构建平衡数据集，实现对三类严重程度（致死/严重，KA；中度/轻微，BC；无伤害，O）的精准预测。结果表明，MambaNet优于ARM-Net，尤其在KA与O类别中表现出更高的精确率、召回率、F1分数和准确率。两者均在区分BC类事故时面临挑战，因特征重叠明显。研究证实了先进表格式深度学习方法与平衡数据集在事故严重性分析中的价值。尽管存在依赖分类变量的局限性，未来可探索连续变量与实时行为数据以提升建模与事故缓解能力。
原文摘要 · Abstract (English)
Child bicyclists (14 years and younger) are among the most vulnerable road users, often experiencing severe injuries or fatalities in crashes. This study analyzed 2,394 child bicyclist crashes in Texas from 2017 to 2022 using two deep tabular learning models (ARM-Net and MambaNet). To address the issue of data imbalance, the SMOTEENN technique was applied, resulting in balanced datasets that facilitated accurate crash severity predictions across three categories: Fatal/Severe (KA), Moderate/Minor (BC), and No Injury (O). The findings revealed that MambaNet outperformed ARM-Net, achieving higher precision, recall, F1-scores, and accuracy, particularly in the KA and O categories. Both models highlighted challenges in distinguishing BC crashes due to overlapping characteristics. These insights underscored the value of advanced tabular deep learning methods and balanced datasets in understanding crash severity. While limitations such as reliance on categorical data exist, future research could explore continuous variables and real-time behavioral data to enhance predictive modeling and crash mitigation strategies.
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