用预训练模型预测电动车碰撞严重程度，提升对重伤害案例的识别能力。
Tabular Data with Class Imbalance: Predicting Electric Vehicle Crash Severity with Pretrained Transformers (TabPFN) and Mamba-Based Models
- 采用TabPFN、MambaNet和MambaAttention三类深度表格模型进行预测
- MambaAttention在严重伤害案例分类上表现最优，准确率达87.3%
- 结合SMOTEENN处理数据不平衡，关键特征包括碰撞速度限值与安全系统
本研究构建了一个深度表格学习框架，基于德克萨斯州2017至2023年的真实交通事故数据，分析纯电动车碰撞事故的严重程度。剔除非电动车后共保留23,301条记录。通过XGBoost和随机森林的特征重要性分析，发现交叉口关系、首次致伤事件、人员年龄、碰撞速度限值及星期几为关键预测因子，同时自动紧急制动等先进安全配置亦具显著影响。针对类别不平衡问题，采用SMOTEENN混合重采样方法。对比了三种前沿深度表格模型：TabPFN、MambaNet与MambaAttention。尽管TabPFN具备良好泛化能力，但基于注意力机制的MambaAttention在严重伤害案件分类中表现更优，准确率达到87.3%。研究结果表明，深度表格架构可有效提升电动车碰撞严重程度预测性能，助力实现数据驱动的安全干预。
原文摘要 · Abstract (English)
This study presents a deep tabular learning framework for predicting crash severity in electric vehicle (EV) collisions using real-world crash data from Texas (2017-2023). After filtering for electric-only vehicles, 23,301 EV-involved crash records were analyzed. Feature importance techniques using XGBoost and Random Forest identified intersection relation, first harmful event, person age, crash speed limit, and day of week as the top predictors, along with advanced safety features like automatic emergency braking. To address class imbalance, Synthetic Minority Over-sampling Technique and Edited Nearest Neighbors (SMOTEENN) resampling was applied. Three state-of-the-art deep tabular models, TabPFN, MambaNet, and MambaAttention, were benchmarked for severity prediction. While TabPFN demonstrated strong generalization, MambaAttention achieved superior performance in classifying severe injury cases due to its attention-based feature reweighting. The findings highlight the potential of deep tabular architectures for improving crash severity prediction and enabling data-driven safety interventions in EV crash contexts.
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