FI-TW: An open train–weather dataset for railway delay analysis in Finland

Abstract

Background Train delays result from complex interactions between operational, technical, and environmental factors. Weather strongly affects railway reliability in Nordic regions, yet publicly available datasets rarely integrate meteorological information with operational train records, which limits research on weather-driven delay. Methods We constructed the Finland Integrated Train-Weather (FI-TW) dataset by combining operational records from the Finland Digitraffic Railway Traffic Service with observations from 209 Finnish Meteorological Institute stations, covering January 2018 to December 2024. Train events and weather measurements were aligned in space and time using the Haversine distance, with a radial fallback strategy that recovers missing parameters from alternative nearby stations. Processing included cyclical encoding of temporal features, robust scaling of weather data to limit the effect of sensor outliers, duplicate removal, and the derivation of weather-scenario indicators together with multi-scale rolling-window aggregations. Results The dataset contains 138 features spanning operational variables and meteorological measurements, and approximately 38.5 million observations from Finland’s 5,915-kilometer rail network. Exploratory analysis shows a clear seasonal structure, with winter delay rates exceeding 25% compared with below 20% in summer, and geographic clustering of high-delay corridors in central and northern Finland. A baseline experiment using extreme gradient boosting (XGBoost) regression reached a mean absolute error of 2.73 minutes for station-specific delay prediction. Conclusions FI-TW is, to the best of our knowledge, the first publicly available dataset that couples Finnish railway operations with synchronized meteorological observations over a seven-year span. It offers multiple target variable formulations and supports applications such as delay prediction, weather impact assessment, seasonal reliability analysis, and infrastructure vulnerability mapping, providing a reusable resource for machine learning research on railway operations in severe northern climates.