arXiv:2601. 16592v2 Announce Type: replace-cross Abstract: Train delays result from complex interactions between operational, technical, and environmental factors.
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The paper presents a machine‑learning approach to predict train delays in Finland using the Finland Integrated Train‑Weather (FI‑TW) dataset, which merges railway operational records with data from about 200 nationwide weather sensors. Three feature sets were tested with XGBoost at Oulu central station: full weather features, instant observations only, and derived weather categories. The category‑based features—hierarchical classes such as Blizzard, Heavy Snow, and Extreme Cold—yielded the best performance, achieving an R² of 0.78, RMSE of 8.5 min, and MAE of 3.7 min, an 11 % R² improvement and 10 % error reduction over the other configurations.
arXiv:2606. 05070v1 Announce Type: new Abstract: Train delay prediction is an important problem for both passengers and railway operators, yet progress in the field remains difficult to assess due to the lack of standardized datasets, prediction targets, and evaluation protocols.
The paper introduces RSTGCN, a Railway-centric Spatio-Temporal Graph Convolutional Network that predicts average arrival delays for all incoming trains at a specific station during a given time period. It incorporates train frequency-aware spatial attention and other architectural innovations to improve predictive accuracy. Experiments on a newly released dataset covering 4,735 Indian Railway stations show RSTGCN outperforms state‑of‑the‑art baselines by 18% in MAE, 14% in MAPE, and 1–8% in RMSE.
arXiv:2610.00370v1 Announce Type: cross Abstract: Station weather forecasting is fundamentally shaped by both complex spatial dependencies across stations and strong physical coupling among weather v...
arXiv:2606. 07044v1 Announce Type: new Abstract: Accurate and coherent passenger demand forecasting is essential for Urban Rail Transit (URT) operations.
arXiv:2608.24665v1 Announce Type: new Abstract: Power outage prediction models are increasingly used in assessments of climate-driven infrastructure risk, yet current evaluation practices obscure whe...