用深度学习分析青少年摩托事故，预测伤情严重程度
Applying Tabular Deep Learning Models to Estimate Crash Injury Types of Young Motorcyclists
- 采用ARMNet和MambaNet模型分析德州10726起事故数据
- ARMNet准确率达87%，对重伤和无伤事故预测效果好
- 结果支持加强头盔监管与针对性安全教育
15至24岁年轻摩托骑手因超速、交通违规及头盔使用不足等因素，面临更高严重事故风险。本研究分析了2017至2022年德克萨斯州10,726起年轻摩托骑手事故数据，采用先进的表格式深度学习模型ARMNet与MambaNet，并结合高级重采样技术应对类别不平衡问题。模型旨在将事故分类为三类：致命或严重伤害、中等或轻微伤害、无伤害。结果显示，ARMNet准确率达到87%，优于MambaNet的86%；两者在预测严重伤害和无伤害事故方面表现优异，但在中等伤害事故分类上存在挑战。研究发现，人口统计、环境及行为因素对事故后果有显著影响。研究强调需制定针对性干预措施，包括加强头盔执法和定制化教育项目，为政策制定者提供基于证据的安全策略支持。
原文摘要 · Abstract (English)
Young motorcyclists, particularly those aged 15 to 24 years old, face a heightened risk of severe crashes due to factors such as speeding, traffic violations, and helmet usage. This study aims to identify key factors influencing crash severity by analyzing 10,726 young motorcyclist crashes in Texas from 2017 to 2022. Two advanced tabular deep learning models, ARMNet and MambaNet, were employed, using an advanced resampling technique to address class imbalance. The models were trained to classify crashes into three severity levels, Fatal or Severe, Moderate or Minor, and No Injury. ARMNet achieved an accuracy of 87 percent, outperforming 86 percent of Mambanet, with both models excelling in predicting severe and no injury crashes while facing challenges in moderate crash classification. Key findings highlight the significant influence of demographic, environmental, and behavioral factors on crash outcomes. The study underscores the need for targeted interventions, including stricter helmet enforcement and educational programs customized to young motorcyclists. These insights provide valuable guidance for policymakers in developing evidence-based strategies to enhance motorcyclist safety and reduce crash severity.
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