GIS-Based Assessment of Shaded Road Segments for Enhanced Winter Risk Management

Abstract

Highlights: What are the main findings? Persistent topographic shading is identified as a primary driver of frost and ice persistence on mountainous road pavements, independent of regional climatic conditions. High-resolution solar incidence modelling, validated with UAV-based thermal orthophotos, reliably detects micro-scale cold-prone road segments. What are the implications of the main findings? Shadow persistence mapping enables proactive winter maintenance planning, allowing targeted de-icing, signage placement, and resource optimization. The methodology provides a decision-support framework for selecting priority road segments for climate-resilient interventions, including geothermal road-heating systems. Winter road safety is critically influenced by microclimatic factors that determine where frost and ice persist on pavement surfaces. Among these, shadow duration plays a decisive yet often under quantified role in mountainous regions, where complex topography and variable solar exposure create localized cold zones. This study presents a GIS-based methodology for detecting and characterizing shadow-prone areas along high-altitude roads, extending previous national-scale models of winter risk toward local, geometry-driven analysis. Using high-resolution Digital Terrain Models (DTM02) and solar radiation simulations, four representative mountain roads (CL-505, AV-501, and CA-820) were analyzed to evaluate how orientation, slope, and surrounding relief control solar incidence. The resulting shadow maps were validated through UAV-derived thermal orthophotos and ground-based temperature measurements, confirming strong correspondence between simulated low-irradiance areas and observed cold surfaces. The integration of geometric and radiometric data demonstrates that topographic shading is a reliable predictor of frost persistence and can be incorporated into winter maintenance planning. By combining high-resolution terrain analysis with empirical thermal validation, this approach not only enhances predictive accuracy but also provides actionable insights for prioritizing road sections at greatest risk. Ultimately, it offers a scalable, data-driven framework for improving infrastructure resilience, optimizing maintenance operations, and mitigating winter hazards in cold-climate mountainous environments, supporting both safety and cost-effectiveness in road management strategies.