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.
By Vinicius Pozzobon Borin, Jean Michel de Souza Sant'Ana, Nurul Huda Mahmood
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.
By Cl\'ement Elliker, Mathis Le Bail, Cl\'ement Mantoux, Jesse Read, Sonia Vanier
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.
By Koyena Chowdhury, Paramita Koley, Abhijnan Chakraborty, Saptarshi Ghosh
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...
By Rongwen Li, Xiao Wang, Mingyang Wang, Hongwu Liu, Changjian Chen, Zhuo Tang, Kenli Li
arXiv:2606. 07044v1 Announce Type: new Abstract: Accurate and coherent passenger demand forecasting is essential for Urban Rail Transit (URT) operations.
By Dang Viet Anh Nguyen, Alma Fazlagic, Kristine Pryds Loft, Filipe Rodrigues
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...
By Yamil Essus, Ranga Raju Vatsavai, Benjamin Rachunok
arXiv:2607. 13101v1 Announce Type: cross Abstract: Global Station Weather Forecasting (GSWF) is pivotal for localized and extreme weather prediction over key regions.
By Songru Yang, Zili Liu, Tao Han, Ben Fei, Fenghua Ling, Lei Bai, Chang Liu, Xiangyang Ji, Zhenwei Shi, Zhengxia Zou
arXiv:2605. 18793v2 Announce Type: replace-cross Abstract: Accurate spatiotemporal pattern analysis is critical in fields such as urban traffic, meteorology, and public health monitoring.
By Jing Chen, Shixiang Pan, Yujie Fan, Haocheng Ye, Haitao Xu, Wenqiang Xu
We present Varda-single-1.0, a medium-range data-driven weather prediction system built for the Alpine domain. It provides hourly deterministic regional forecasts on a mesh of 1 km resolution and glob...
UniGIO is a generative framework that models global in‑situ weather dynamics directly from incomplete GIO data, unifying forecasting, imputation, and generation across arbitrary missing ratios. It employs an Observation Mixer, Event Aligner, Adaptive Temporal Mixer, and a Mixture‑of‑Experts structure to capture station‑level complementarity, temporal dependencies, and extreme events, refining outputs with a Local Refiner. Experiments on the Weather‑5K dataset show state‑of‑the‑art performance, improving accuracy, fidelity, and extreme event capture by 11%, 12%, and 5% respectively.
By Songru Yang, Zili Liu, Tao Han, Ben Fei, Lei Bai, Chang Liu, Zhengxia Zou, Xiangyang Ji, Wanli Ouyang, Zhenwei Shi
Varda‑single‑1.0 is a medium‑range, data‑driven weather prediction system designed for Switzerland’s Alpine region, delivering hourly deterministic forecasts at 1 km resolution and global forecasts at 31 km. It uses two independently trained stretched‑grid Graph Transformer models—an autoregressive forecaster and a temporal downscaler—trained through a curriculum that starts with ERA5 reanalysis, proceeds to a 20‑year regional reanalysis, and ends with fine‑tuning on operational analyses. Over a one‑year verification, Varda‑single matches or surpasses MeteoSwiss’s numerical weather prediction baselines for most key metrics, though it underestimates local wind peaks and produces smoother convective precipitation fields.
whyItMatters":"The system demonstrates that high‑resolution machine‑learning models can rival traditional numerical weather prediction in complex terrain, offering a complementary tool for operational forecasting and research."
By Alberto Pennino, Francesco Zanetta, Michele Cattaneo, Claire Merker, Radi Radev, Jonas Bhend, Louis Frey, Hugues de Laroussilhe, Oph\'elia Miralles, Carlos Osuna, Daniele Nerini, Andreas Pauling, Daniel Hupp, Ulrich Hamann, Mary McGlohon, Marti Bosch, Luca Lanzilao, Marco Arpagaus, Lukas Jansing, Daniel Leuenberger, Mark A. Liniger, Katrin Ehlert, Matthew Chantry, H\aa{}vard Homleid Haugen, Gert Mertes, Ana Prieto Nemesio, Mario Santa Cruz, Jasper Wijnands, Gabriel Moldovan, Harrison Cook, Oliver Fuhrer
arXiv:2604. 20822v2 Announce Type: replace-cross Abstract: The offshore wind energy sector is expanding rapidly, increasing the need for independent, high-temporal-resolution monitoring of infrastructure deployment and operation at global scale.
By Thorsten Hoeser, Felix Bachofer, Claudia Kuenzer