统一图形神经网络因果机器学习和符合性预测,用于铁路运输系统中强大的因果推理
1Department of Electric-Electronics Engineering, Haliç University, Istanbul, Turkey. mehmettaciddinakcay@halic.edu.tr.
Scientific reports
|November 7, 2025
概括
本研究引入了一种结合图形神经网络,因果机器学习和铁路运输系统规范预测的新框架. 它改善了因果效应估计,并为操作决策提供了可靠的不确定性.
科学领域:
- 运营研究 运营研究
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 铁路运输系统具有复杂的运营动态.
- 现有的模型在网络依赖性,因果推理和不确定性量化方面扎.
- 当前的方法经常导致简单,不清楚或过度自信的模型.
研究的目的:
- 开发一个全面的框架,整合图形神经网络 (GNN),因果机器学习 (CML) 和符合预测 (CP).
- 解决网络范围内的依赖关系,区分因果关系和因果关系,量化铁路运输中的预测不确定性.
- 从反应性数据建模转向铁路运营的积极主动,战略决策支持.
主要方法:
- 利用GNN来捕获网络拓和依赖关系.
- 使用CML进行操作干预的客观因果影响评估.
- 适用于数学上保证的,无分布的不确定性间隔的应用CP.
主要成果:
- 用GNN增强的模型在总量预测任务中实现了高性能 (CV R2 ≈ 0.87).
- 在CML-CP框架显著减少因果效应估计误差 (CV MAE: 124,758.04).
- 在预测和因果推理任务之间展示了明显的性能差异化.
结论:
- 综合框架为先进的铁路运输建模提供了一份方法路线图.
- 能够通过强大的因果洞察和不确定性保证,进行可靠的"假如"情景分析.
- 通过积极的决策支持,促进更具弹性和高效的铁路运营.
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