对安全气候分析的一种可解释的集群方法:检查驾驶员组区别
Kailai Sun1, Tianxiang Lan1, Yang Miang Goh1
1National University of Singapore, Singapore.
Accident; analysis and prevention
|December 30, 2023
概括
本研究介绍了用于卡车司机安全气候分析的可解释机器学习,确定了监督照顾等关键因素,以改进事故预防策略.
科学领域:
- 职业安全与健康问题 职业安全与健康问题
- 运输安全运输安全
- 数据科学和机器学习
背景情况:
- 卡车运输行业面临着大量的工作场所事故和死亡事故,大型卡车参与了大量的交通死亡.
- 安全环境被认为是预防事故的关键,但根据安全感知对员工进行分类仍未得到充分探索.
- 现有的研究往往缺乏算法比较和可解释的方法来理解影响员工安全感知的因素.
研究的目的:
- 引入一种可解释的集群方法来分析卡车司机的安全气候感知.
- 将五个聚类算法进行比较,并提出一种用于评估部分依赖图 (QPDP) 的新方法.
- 为了提高集群结果的可解释性,使用机器学习技术,如Shapley增量解释和 permutation 功能的重要性.
主要方法:
- 通过使用五种算法,对7000多名美国卡车司机的安全气候感知进行集群分析.
- 开发和应用一个定量部分依赖图 (QPDP) 方法的可解释性.
- 使用可解释的机器学习措施 (沙普利增量解释,换特征重要性,QPDP) 来解释集群成员资格.
主要成果:
- 根据他们对安全气候的看法,确定了不同的卡车司机集群.
- 突出了监督照顾的促进,作为区分驾驶员群体的关键因素.
- 证明了可解释机器学习在理解安全气候变化的有效性.
结论:
- 该研究提供了一种创新的,可解释的集群方法,用于卡车运输行业的安全气候分析.
- 调查结果强调了监督护理在塑造驾驶员安全感知方面的重要性,并建议有针对性的干预措施.
- 机器学习技术,特别是集群分析,为推进职业安全科学知识提供了有价值的工具.
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