Integrating Meteorological and Operational Data: A Novel Approach to Understanding Railway Delays in Finland

TL;DR

This paper introduces a comprehensive dataset combining Finnish railway operational data with meteorological observations, enabling advanced analysis of weather impacts on train delays and supporting machine learning applications in railway reliability studies.

Contribution

The study presents the first publicly available dataset integrating Finnish railway operations with synchronized meteorological data, along with preprocessing methods and initial analysis demonstrating its utility.

Findings

Winter delays exceed 25% during peak months

High-delay corridors are geographically clustered in Finland

Baseline machine learning model predicts delays with MAE of 2.73 minutes

Abstract

Train delays result from complex interactions between operational, technical, and environmental factors. While weather impacts railway reliability, particularly in Nordic regions, existing datasets rarely integrate meteorological information with operational train data. This study presents the first publicly available dataset combining Finnish railway operations with synchronized meteorological observations from 2018-2024. The dataset integrates operational metrics from Finland Digitraffic Railway Traffic Service with weather measurements from 209 environmental monitoring stations, using spatial-temporal alignment via Haversine distance. It encompasses 28 engineered features across operational variables and meteorological measurements, covering approximately 38.5 million observations from Finland's 5,915-kilometer rail network. Preprocessing includes strategic missing data handling through spatial fallback algorithms, cyclical encoding of temporal features, and robust scaling of weather data to address sensor outliers. Analysis reveals distinct seasonal patterns, with winter months exhibiting delay rates exceeding 25\% and geographic clustering of high-delay corridors in central and northern Finland. Furthermore, the work demonstrates applications of the data set in analysing the reliability of railway traffic in Finland. A baseline experiment using XGBoost regression achieved a Mean Absolute Error of 2.73 minutes for predicting station-specific delays, demonstrating the dataset's utility for machine learning applications. The dataset enables diverse applications, including train delay prediction, weather impact assessment, and infrastructure vulnerability mapping, providing researchers with a flexible resource for machine learning applications in railway operations research.