How to Incorporate Weather Variability and Climatic Trends in Winter Road Maintenance Simulations

Abstract Winter road maintenance (WRM) is crucial for traffic safety and mobility in regions that can experience temperatures below 0°C. However, annual weather variability and evolving climate trends introduce uncertainty into WRM planning. This study proposes a method for integrating weather variability and climatic trends into WRM simulations provided by multiscale geographically weighted regression (MGWR) Effort Model, a regression-based model developed for estimating winter maintenance efforts in Norway. Focusing on a case study in Trondheim city, Norway, historical time series of four weather variables, including snow days, cold days, fluctuation days, and freezing rain days, were analyzed for trends and cyclic patterns over the period 1957–2019. These fitted trends were then used to predict future weather inputs, which were subsequently fed into the MGWR Effort Model to estimate future winter operations. Comparison with actual data from 2020 to 2023 shows that trend-based simulations predict average future maintenance operations with acceptable precision, particularly for plowing operations (89%). Although the year-to-year variability remains difficult to capture, the findings support the method’s potential to enhance WRM planning under climate uncertainty, providing decision makers with a practical framework for anticipating operational needs over upcoming contract periods.