AI IMPACT ANALYSIS
Integrating Meteorological and Operational Data: A Novel Approach to Understanding Railway Delays in Finland
This study introduces the first publicly available dataset integrating Finnish railway operational data with synchronized meteorological observations from 2018-2024. Covering approximately 38.5 million observations across Finland's 5,915-kilometer rail network, the dataset facilitates comprehensive analysis of weather impacts on railway performance. Exploratory analysis reveals distinct seasonal patterns, with winter months exhibiting delay rates exceeding 25% and geographic clustering of high-delay corridors. A baseline XGBoost experiment achieved a Mean Absolute Error of 2.73 minutes for predicting station-specific delays, demonstrating the dataset's utility for machine learning applications and infrastructure vulnerability mapping.
Key Metrics from the Research
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Total Observations
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Rail Network Length
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MAE in Delay Prediction
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Environmental Stations
Deep Analysis & Enterprise Applications
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Explore the structure, scope, and meticulous processing steps involved in creating this novel integrated railway and weather dataset for Finland.
Vast Data Coverage
38.5M Total ObservationsThe dataset encompasses approximately 38.5 million observations from Finland's 5,915-kilometer rail network, integrating operational metrics with weather measurements from 209 environmental monitoring stations.
Data Integration Methodology
The methodology for constructing this integrated dataset involved sequential steps from data acquisition to final dataset assembly, including spatial and temporal alignment, missing data mitigation, and feature engineering.
Train Dataset
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Weather Dataset
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Initial Merged Dataset
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Scaling Weather Data
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Missing Data Mitigation Strategy
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Splitting Data (train/test)
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Dropping Unnecessary Columns
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Removing Duplicates
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Handling Missing Data
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Converting Temporal Features to Cyclical
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Final Dataset: FI-TW
Weather Feature Missingness
Systematic sensor failures and uneven deployment across the 209 environmental monitoring stations resulted in varying degrees of missingness for weather features, necessitating robust mitigation strategies. For instance, precipitation amount is missing in 86.91% of observations.
| Feature | Missing (%) |
|---|---|
| Precipitation amount | 86.91% |
| Cloud amount | 33.85% |
| Wind direction | 33.72% |
| Pressure (msl) | 18.10% |
| Dew-point temperature | 8.35% |
| Air temperature | 8.25% |
| Relative humidity | 8.20% |
| Snow depth | 6.17% |
| Precipitation intensity | 6.02% |
| Horizontal visibility | 5.84% |
| Wind speed | 5.59% |
| Gust speed | 5.58% |
Discover the critical insights derived from the dataset, including seasonal delay patterns, severity distributions, and the impact of extreme weather.
Winter Delay Rates Exceed 25%
>25% Winter Delay RateTemporal analysis reveals distinct seasonal patterns, with winter months (Dec-Jan-Feb) experiencing substantially higher delay percentages, often exceeding 25%, compared to below 20% in summer months.
Delay Severity Distribution
Over seven years (2018-2024), medium delays (10-15 minutes) are the most prevalent, accounting for 49% of occurrences, indicating common moderate service disruptions.
| Delay Severity Category | Number of Days | Percentage |
|---|---|---|
| Low (5-10 minutes) | 300 | 11.73% |
| Medium (10-15 minutes) | 1254 | 49.0% |
| High (15-20 minutes) | 597 | 23.35% |
| Very High (20+ minutes) | 405 | 15.84% |
Impact of Extreme Finnish Weather on Rail Operations
Finland's extreme climate poses particularly severe challenges. Winter temperatures reaching -40°C can cause mechanical failures in automatic doors, couplings, and switching systems. Heavy snowfall disrupts signaling equipment and requires extensive track clearing operations. During autumn, fallen leaves create slippery layers on rails, reducing adhesion and requiring trains to operate at lower speeds for safety. These weather-related issues often cascade through the interconnected rail network, amplifying delays across multiple routes.
Envision how this dataset can fuel future research in railway operations, from advanced predictive models to causal inference and real-time systems.
Leveraging 6G and AI for Predictive Maintenance
The convergence of advanced wireless communication technologies (5G/6G) and artificial intelligence (AI) presents unprecedented opportunities for transforming railway operations. Enhanced connectivity facilitates real-time, high-bandwidth data collection from distributed sensors across railway infrastructure and station facilities. Combined with modern AI techniques, these data streams enable railway operators to anticipate potential failures, optimize maintenance schedules, and significantly improve service reliability and safety across the entire network.
XGBoost Achieves 2.73 Min MAE
2.73 min MAE in Delay PredictionA baseline XGBoost regression experiment demonstrated the dataset's utility, achieving a Mean Absolute Error of 2.73 minutes for predicting station-specific delays on the Oulu asema route. This indicates strong potential for advanced machine learning applications and serves as a benchmark for future model development.
Key Research Avenues
Several promising research directions emerge from this work, leveraging the integrated dataset for more sophisticated analyses and real-world applications.
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