预测荷兰铁路网中的晚点列车轨迹：基于树集成方法的地形、运营和天气特征的可解释人工智能评估
Predicting Delayed Train Trajectories on the Dutch Railway Network: Explainable AI Evaluation of Topological, Operational and Weather Features with Tree Based Ensemble Methods
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中文总结 AI 辅助
本研究利用可解释的树集成模型预测荷兰铁路网列车晚点，发现丰富特征虽提升同期预测精度，但跨月预测性能衰减，归因于环境特征波动，需转向动态季节感知架构。
中文摘要 AI 辅助
可靠预测客运列车晚点是铁路管理的关键组成部分。虽然当代研究经常通过部署不透明的深度学习架构来追求绝对精度的最大化，但驱动纵向预测性能衰减的底层数据机制仍未得到充分探索。因此，本研究提供了对全网铁路晚点预测的可解释时间鲁棒性分析。本研究聚焦于荷兰铁路网，利用可解释的基于树的集成方法来整合细粒度的地形、环境和运营特征。总体发现表明，虽然特征丰富的基于树的模型能改善同期（月内）预测，但在跨非同期（未来月份）评估时，预测性能会系统性下降。此外，多时间范围的SHAP和离散度分析明确地将这种性能下降与环境特征的波动性和统计目标定义内部的不稳定性联系起来。最终，本论文表明，仅靠更丰富的特征集不足以解决长期预测的约束，强调了向以绝对运营边界为锚定的动态、季节感知架构过渡的必要性。
英文摘要
The reliable prediction of passenger train delays is a critical component of railway management. While contemporary research frequently attempts to maximize absolute accuracy by deploying opaque deep learning architectures, the underlying data mechanics driving longitudinal predictive decay remain underexplored. Consequently, this study provides an explainable temporal robustness analysis of network-wide railway delay prediction. Focusing on the Dutch railway network, this research utilizes interpretable tree-based ensembles to integrate granular topological, environmental, and operational features. The overarching finding establishes that while feature-rich tree-based models improve simultaneous (within-month) prediction, predictive performance systematically degrades when evaluated across non-simultaneous (future) months. Furthermore, multi-horizon SHAP and dispersion analyses explicitly link this degradation to environmental feature volatility and instability within the statistical target definition. Ultimately, this study demonstrates that richer feature sets alone are insufficient to resolve long-term forecasting constraints, underscoring the necessity to transition toward dynamic, season-aware architectures anchored by absolute operational boundaries.
