Railway Incident Prediction via ML
Analyzing the correlation between weather conditions and railway incidents to predict daily defects using machine learning.
Business Context
SNCF, France's national railway company, faces weather-related infrastructure incidents that disrupt operations. Predicting these defects would enable preventive maintenance.
Strategic Problem
How to analyze the correlation between weather conditions and the frequency of railway incidents, then use machine learning to predict daily defects?
Data Sources
Historical weather data (temperature, precipitation, wind), railway incident logs, track maintenance records, and geographic infrastructure mapping.
Methodology
Built correlation models between weather variables and incident frequency. Trained ML models to predict daily defect probabilities. Validated against historical incident data.
Key Results
Delivered a predictive model capable of forecasting daily railway defect likelihood based on weather forecasts. Reached finalist stage.
Business Impact
Framework applicable to any infrastructure operator seeking to reduce weather-related disruptions through predictive analytics.