Road infrastructure is the backbone of modern transportation, yet its degradation is a growing global concern. Increasing traffic volumes, extreme weather conditions, and aging road surfaces are accelerating wear and tear, leading to costly repairs and safety hazards. Traditional maintenance approaches often rely on reactive interventions, addressing issues only after they become critical. But what if we could predict road deterioration before it happens?
In our latest whitepaper, "Use of Forecasting Models to Improve Predictive Maintenance Strategies," experts from NIRA Dynamics and the Technical University of Denmark (DTU) explore how advanced data analytics and machine learning can revolutionize road maintenance.
Modern vehicles are more than just modes of transport, they are moving data hubs. NIRA Dynamics leverages connected vehicle data to continuously monitor road conditions. By collecting and analyzing information on road roughness, friction levels, anomalies, and traffic patterns, predictive models can identify emerging maintenance needs well before visible deterioration occurs.
Using historical connected vehicle data, combined with weather patterns and traffic levels, researchers have developed machine learning models that accurately predict changes in road conditions. The study outlined in the whitepaper tested various forecasting techniques, including:
Linear Models – Simple yet effective in identifying direct correlations.
Normalized Linear Models (NLinear) – Improved accuracy by adjusting for distribution shifts.
Decomposition-Based Models (DLinear) – Enhanced flexibility in detecting trends and seasonal variations.
Fast Iterative Filtering for Time Series (FITS) – A complex approach that captures deeper temporal dependencies.
Results indicate that even the simplest models can deliver precise forecasts, enabling road authorities to plan maintenance activities with greater efficiency and reduced costs.
By applying these forecasting techniques, cities and transportation agencies can move from reactive maintenance strategies to proactive road management. This transition not only minimizes disruption and extends pavement lifespans but also improves road safety and optimizes budget allocation. As data collection continues to expand, predictive maintenance models will only become more powerful, further refining infrastructure planning and sustainability.
Are you ready to explore the future of predictive road maintenance? Download our whitepaper today to gain deeper insights into the methodologies, findings, and real-world applications of these forecasting models. Learn how connected vehicle data and machine learning are transforming road management, paving the way for safer, smarter, and more cost-effective infrastructure solutions.