发表机构
Centre for Wireless Communications, University of Oulu（奥卢大学无线通信中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究利用芬兰FI-TW数据集和XGBoost，通过基于领域知识的天气类别特征预测列车延误，在奥卢站实现R²为0.78，较原始气象特征提升11%性能，验证了边缘部署的高效性。
AI 中文摘要
可靠的铁路运营日益依赖于通过无线传感器基础设施提供的实时环境情报，而6G网络将通过集成感知和边缘计算大幅增强这一能力。恶劣天气，尤其是在具有极端温度和强降水的北极地区，仍然是导致列车延误的主要原因，然而大多数预测方法依赖于原始气象输入，而未利用基于领域知识的特征工程。本文研究了利用芬兰综合列车-天气（FI-TW）数据集进行列车延误预测的机器学习方法，该数据集将铁路运营记录与芬兰气象研究所全国传感器网络（约200个站点，通过无线链路通信）的观测数据相融合。我们使用XGBoost在奥卢中央车站（101,146个观测值）评估了三种特征配置：完整天气特征、仅即时天气观测以及派生天气类别场景。基于类别的方法采用分层分类，如暴风雪、大雪和极寒，实现了R²为0.78，均方根误差为8.5分钟，平均绝对误差为3.7分钟，与替代配置相比，R²提高了11%，误差降低了10%。这些结果表明，源自传感器流的紧凑、领域信息特征优于原始气象观测，提供了适合在现有和新兴无线基础设施上进行边缘部署的带宽高效表示。
英文摘要
Reliable railway operations depend increasingly on real-time environmental intelligence delivered through wireless sensor infrastructures, a capability that 6G networks will substantially enhance through integrated sensing and edge computing. Adverse weather, particularly in Arctic regions with extreme temperatures and heavy precipitation, remains a leading cause of train delays, yet most prediction approaches rely on raw meteorological inputs without exploiting domain-informed feature engineering. This paper investigates machine learning for train delay prediction using the Finland Integrated Train-Weather (FI-TW) dataset, which fuses railway operational records with observations from the Finnish Meteorological Institute's nationwide sensor network of approximately 200 stations communicating over wireless links. We evaluate three feature configurations using XGBoost at Oulu central station (101,146 observations): full weather features, instant weather observations only, and derived weather category scenarios. The category-based approach, employing hierarchical classifications such as Blizzard, Heavy Snow, and Extreme Cold, achieved an R^2 of 0.78, root mean squared error of 8.5 minutes, and mean absolute error of 3.7 minutes, representing an 11% R^2 improvement and 10% error reduction over alternative configurations. These results demonstrate that compact, domain-informed features derived from sensor streams outperform raw meteorological observations, offering bandwidth-efficient representations suitable for edge deployment over current and emerging wireless infrastructures.
Comments6 pages, 3 Figures, 4 tables, presented at Wireless Europe 2026, Rimini, Italy, June 2026
