基于动态因果权衡的风险传播建模,用于机场移动区域
Wei Wu1,2, Jiayi Lin3, Ming Wei3
1School of Traffic and Transportation, Beijing Jiaotong University, Beijing, 100044, China. wwu@cauc.edu.cn.
Scientific reports
|January 14, 2026
概括
本研究引入了一种使用复杂网络理论和强化学习来分析机场风险传播的新型模型. 它通过动态因果强度分析识别关键因素并将风险扩散降低20%.
科学领域:
- 航空安全航空安全
- 复杂系统分析 复杂系统分析
- 交通运输中的人工智能
背景情况:
- 动态运营条件对机场移动区域的安全有重大影响.
- 了解风险传播对于预防事件至关重要.
- 现有的模型与时间变化的因素和数据稀缺性作斗争.
研究的目的:
- 开发一个模型来分析机场移动区域的动态风险传播.
- 解码操作条件和事件之间的因果关系.
- 确定关键的风险因素,并实现有针对性的控制策略.
主要方法:
- 综合复杂网络理论和因果卷积强化学习 (CCRL).
- 构建具有异质节点分类 (阻抗/累积) 的风险传播网络.
- 使用增强的灰色关系模型来识别关键风险因素和CCRL用于动态传播重量调整.
主要成果:
- 在数据稀缺的情况下,拟议的模型在预测准确性方面比动态贝叶斯网络 (DBN) 优于4.1%.
- 确定了63个影响机场移动区域安全性的关键风险因素.
- 通过有针对性的控制策略,实现了风险扩散指数的20%降低.
结论:
- 这种基于CCRL的新型模型有效地解构了机场移动区域中随时间变化的风险传播.
- 该方法提供了一种可靠的方法来识别关键风险因素并减轻其影响.
- 在航空安全分析和运营风险管理方面取得了重大进展.
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