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

Abstract

Accurate and timely forecasts of road surface conditions are crucial for efficient winter maintenance, enhanced traffic safety, and the optimized use of de-icing agents. Road surface phenomena, in complex fields present challenges to traditional forecasting methods due to their nonlinear and localized nature. This study presents a machine learning framework predicting real-time road states (dry, wet, icy, snowy) across Bavaria, Germany. It integrates data from over 516 Road Weather Stations (RWS), thermal measurements from winter maintenance vehicles, and elevation data from the Open Elevation API. Data undergoes temporal alignment, spatial interpolation, and missing-value imputation. Decision Trees form the core model for interpretability and nonlinear pattern handling. Each RWS employs a localized model, while a generalized version covers unmonitored roads via spatial adjustments. With over 85% accuracy, the system facilitates dynamic winter maintenance and minimizes resource waste. Cyber-physical in smart mobility and transportation networks support improved real-time hazard responses. This approach shows how scalable infrastructure can be made resilient using machine learning.

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Titel	AI-based System for Road Surface Condition Forecasting Using Multi-Source Meteorological Data
Medien	Procedia Computer Science
Verlag	ELSEVIER
Herausgeber	Francesco Longo, Vittorio Solina, Emmanuel Francalanza
Band	277, 2026
Verfasser	Prof. Dr. Heike Markus, Sampat Acharya, Shantall Marucia Cisneros Saldana, Rudolf Lehmann, Dr. Ali Fallah Tehrani
Seiten	1269-1278
Veröffentlichungsdatum	23.03.2026
Projekttitel	Glätte2
Zitation	Markus, Heike; Acharya, Sampat; Cisneros Saldana, Shantall Marucia; Lehmann, Rudolf; Fallah Tehrani, Ali (2026): AI-based System for Road Surface Condition Forecasting Using Multi-Source Meteorological Data. Procedia Computer Science 277, 2026, 1269-1278.