一种新的双层集群优化方法,以平衡处理崩数据的方法
Tanveer Ahmed1, Vikash V Gayah1
1Department of Civil and Environmental Engineering, The Pennsylvania State University, University Park, PA 16802, United States.
Accident; analysis and prevention
|May 18, 2025
概括
一种新的双级集群优化 (BLCO) 方法有效地平衡处理和控制站点,用于安全对策分析. 这种方法显著减少了观察数据的偏差,改善了交通安全评估.
科学领域:
- 交通安全研究 交通安全研究
- 统计建模 统计建模
- 数据分析 数据分析
背景情况:
- 评估安全对策至关重要,但由于观测数据存在偏差,因此具有挑战性.
- 处理和控制地点之间的道路特征不平衡可能会扭曲对抗措施有效性估计.
- 现有的倾向评分方法可能无法完全减少偏差,这在很大程度上取决于模型的制定.
研究的目的:
- 引入一种新的双层聚类优化 (BLCO) 方法,用于匹配治疗和控制站点.
- 在交通安全研究中,尽量减少各组之间的协变不平衡.
- 使用观测数据提高治疗效果估计的准确性.
主要方法:
- 开发了一种使用竞争性学习的双级集群优化 (BLCO) 方法.
- BLCO将共同变量的标准化偏差的平方和最小化.
- 将BLCO与倾向性得分匹配 (logit,随机森林) 和遗传匹配进行比较.
主要成果:
- 在平衡共变量方面,BLCO显著超过了基准方法.
- 与未匹配数据相比,减少了96.16%的平均绝对标准化偏差.
- 与倾向性得分匹配相比,实现了88.76%的改善,模型更适合治疗效应.
结论:
- BLCO方法提供了一种强大而有效的方法来减少观察性交通安全数据的偏差.
- 它有效模拟随机试验条件,改善治疗效果估计.
- 该方法可以适应各种数据集大小和不同领域的高维共变量.
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