可解释出租车出发时间预测出发航班使用堆叠合奏学习和SHAP分析.
Tao Wu1, Yanfeng Mao1, Junchuan Huang2
1China Academy of Civil Aviation Science and Technology, Beijing, China.
Scientific reports
|February 20, 2026
概括
本研究引入了一种新的出租车停机时间预测模型,使用堆叠集体学习和SHAP分析来提高空中交通管理的准确性和可解释性.
科学领域:
- 航空运营研究 航空运营研究
- 交通运输中的人工智能
- 航空交通管理系统 航空交通管理系统
背景情况:
- 现有的出租车停车时间预测模型的解释性和概括性很差.
- 准确预测出租车停机时间对于高效的机场运营和空中交通管制至关重要.
研究的目的:
- 开发一种新的出租车停车时间预测模型,增强可解释性和概括性.
- 将出租车停车时间分解并分析影响因素,以提高预测准确度.
- 为了利用堆叠集体学习和Shapley增量解释 (SHAP) 来进行模型开发和验证.
主要方法:
- 将出租车停车时间分解为不受阻碍和动态的组件.
- 对每个组成部分的影响因素的相关性分析.
- 构建一个基于堆叠的预测模型,比较整体和分阶段方法.
- 实施SHAP分析以量化特征重要性和模型可解释性.
主要成果:
- 无障碍出租车停车时间主要受机场配置的影响;动态出租车停车时间受地面交通流的影响.
- 与整体预测相比,分阶段预测提供了更好的解释性,性能略低 (MAPE:12.0%).
- 堆叠模型表现出卓越的准确性 (41.0%在±60秒内) 和概括性.
- 双重特征选择机制 (SHAP和相关性分析) 提高了预测准确性,并减少了特征尺寸.
- SHAP分析有效地解释了特征影响和相互作用,揭开了模型的神秘性.
结论:
- 建议的堆叠模型与SHAP分析显著改善出租车停车时间预测的准确性和可解释性.
- 分解出租车停机时间和采用分阶段预测为空中交通管制员提供了宝贵的见解.
- 该模型提供可操作的智能,以优化机场地面操作和决策.
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