一个混合机器学习增强的MCDM模型用于运输安全工程
Xingjian Zhang1, Haowen Chen2, Jingxuan Chen3
1Courant Institute of Mathematical Sciences, New York University, New York, NY, 10012, USA.
Scientific reports
|October 20, 2025
概括
这项研究引入了一种新的DCRITIC-WASPAS-K-means模型,用于在运输安全方面的多标准决策. 它提高了可靠性,并减少了计算时间,以便做出更好的政策决策.
科学领域:
- 工程 工程师 工程师 工程师
- 计算机科学 计算机科学
- 运营研究 运营研究
背景情况:
- 多标准决策 (MCDM) 对于运输安全工程至关重要.
- 传统方法面临数据不确定性和计算效率的挑战.
研究的目的:
- 为强大的决策建议提出混合机器学习增强的MCDM模型.
- 提高交通安全决策过程的可靠性和效率.
主要方法:
- 基于距离相关的标准的整合 通过标准间相关性 (DCRITIC) 进行标准权重.
- 应用权重总和产品评估 (WASPAS) 进行决策聚合.
- 利用基于图形的机器学习技术来增强K-means聚类以进行强大的中心点选择,创建DCRITIC-WASPAS-K-means模型.
主要成果:
- DCRITIC-WASPAS-K-means模型在决策结果中表现出更好的稳定性和可靠性.
- 机器学习集成减少了与K-means集群中的初始中心点选择相关的不确定性,减少代和运行时间.
- 美国国家组织 (OAS) 地区的一项案例研究证实了该模型的实际实用性和卓越性能.
结论:
- 拟议的模型提供了一个可靠,可扩展和数据驱动的工具,用于战略规划和资源分配在运输安全.
- 它通过提供一致的决策成果和有效的政策影响沟通来提高政策干预的可信度.
- 公共管理人员,政策制定者和政府机构可以利用这一框架,在不确定的环境中改善决策.
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