加强工作区撞车严重性分析:合成少数群体过量抽样技术在平衡少数群体类别中的作用
Muhammad Adeel1, Asad J Khattak1, Sabyasachee Mishra2
1Department of Civil and Environmental Engineering, The University of Tennessee, Knoxville, TN, United States.
Accident; analysis and prevention
|September 28, 2024
概括
攻击性驾驶,超速和醉酒驾驶增加了道路工作区的撞车严重程度. 解决不平衡的伤害数据揭示了导致因素的转变,有助于改善安全.
科学领域:
- 运输工程 运输工程
- 交通安全 交通安全
- 数据科学数据科学数据科学
背景情况:
- 道路工程区 (WZ) 由于交通条件的改变和可见度的降低,造成了重大安全风险.
- 了解WZ碰撞伤害严重程度的人类行为因素至关重要,特别是不平衡的碰撞数据.
- 在伤害严重程度分析中常见的不平衡数据集,往往低于严重的结果,需要先进的技术.
研究的目的:
- 调查在道路工程区 (WZ) 发生的撞车事故中导致受伤严重程度的行为因素.
- 分析这些因素在解决数据不平衡后如何变化,使用合成少数群体过量采样技术 (SMOTE).
- 为分析不平衡的碰撞伤害数据提供一个框架.
主要方法:
- 利用了田纳西州7855起WZ撞车事故 (2018-2022) 的数据集.
- 应用频率主义方法和采用SMOTE的机器学习方法来处理数据不平衡.
- 在平衡少数伤害严重程度类别后,研究了促成因素的重要性的变化.
主要成果:
- 攻击性驾驶,超速和醉酒驾驶被确定为增加伤害严重性的重要因素.
- 平衡不平衡的数据集改变了各种因素对事故伤害严重性的相对重要性.
- 该研究表明,SMOTE有效地改善了从不平衡的撞车数据中得出的推断.
结论:
- 攻击性驾驶和驾驶障碍等行为因素对WZ碰撞伤害严重程度有很大影响.
- 调整数据不平衡对于全面了解碰撞伤害严重程度至关重要.
- 这些发现为交通安全工程师和政策制定者提供了宝贵的见解,以加强WZ安全措施.
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