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The research aims to improve the prediction of road surface conditions using AI, integrating diverse meteorological data sources.

AI-based System for Road Surface Condition Forecasting Using Multi-Source Meteorological Data

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The research aims to improve the prediction of road surface conditions using AI, integrating diverse meteorological data sources.
Developed a machine learning framework to predict road states (dry, wet, icy, snowy) in real-time.
Integrated data from over 516 Road Weather Stations, winter maintenance vehicles, and elevation data.
Employed decision trees for modeling, focusing on interpretability and handling nonlinear patterns.
Applied localized models for each Road Weather Station and a generalized model for unmonitored roads.
Achieved over 85% accuracy in predicting road surface conditions.
Facilitated dynamic winter maintenance that minimizes resource waste.
Demonstrated the scalability and resilience of infrastructure using machine learning in smart mobility.

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Develops an AI system predicting road surface conditions in Bavaria, indicating enhanced winter maintenance efficiency.

Saldana et al. (2026) studied this question.

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