Integrating Meteorological and Operational Data: A Novel Approach to Understanding Railway Delays in Finland. arXiv preprint, cs.LG, CC BY 4.0, January 2026.
I co-authored this preprint, which introduces the first publicly available dataset linking Finnish railway operations with synchronized meteorological data, 2018 to 2024: approximately 38.5 million observations across the 5,915 km rail network and 209 weather stations.
Alongside the dataset, we established a baseline: an XGBoost model reaching 2.73-minute mean absolute error for station-level delay prediction, demonstrating the dataset’s value for machine-learning applications in railway operations.
The work grew out of my research internship at the Centre for Wireless Communications, University of Oulu, on the project “Artificial Intelligence for Predictive Modelling of Weather-Induced Delays in Finland’s Railway System”, supervised by Vinicius Pozzobon Borin, PhD, and Dr. Nurul Huda Mahmood.