Asphalt Model Top-Layer (1A)
Accurate forecasting of condition characteristics is essential for effective and sustainable road asset management. Autobahn GmbH des Bundes collects road condition data every four years using the ZEB procedure (Zustandserfassung und -bewertung). While traditional approaches like the Curve-Shifting method provide a proactive maintenance framework, they offer limited precision and cannot learn from new data. As a result, they are not always sufficient for reliably forecasting future road conditions. The challenge lies in creating more accurate forecasting tools.
This pilot project focuses on the development and validation of innovative forecasting models for road condition characteristics. Accurate forecasting of condition characteristics is essential for effective and sustainable road asset management. The models leverage historical inspection data from both permanent performance sections, measured twice a year, and the broader national road network.
The best-performing models were selected for demonstration.
The current implementation phase includes ongoing research to determine the technical benefits between the existing forecasting method and new forecasting methods and to identify ways in which research results can be communicated and placed in the professional world. Furthermore, the aim is to show whether and how results can be integrated at the infrastructure operator Autobahn GmbH des Bundes.
Once validated, calibrated, and implemented, these data-driven models have the potential to provide more accurate and actionable forecasts. They could help Autobahn GmbH des Bundes optimize maintenance planning, budgeting, and interventions, supporting smarter, data-driven asset management.
