Large-scale Geographic Information System-based Vulnerability Assessment of Black-ice-related Expressway Accidents Using Entropy Weighting

Black ice is difficult to detect with the naked eye because it reflects the color of the road surface.It often forms rapidly owing to freezing rain and leads to severe traffic accidents, particularly on expressways.To reduce the risk of casualties, it is essential to assess the vulnerability of expressway sections to black-ice-related accidents.In his study, we present a large-scale assessment of such vulnerability, aimed at providing a quantitative foundation for preventive measures and rational resource allocation.Ten contributing factors were selected using the Intergovernmental Panel on Climate Change (IPCC) Climate Change Vulnerability Assessment Framework and categorized into three groups: exposure, sensitivity, and adaptive capacity.Each factor was normalized and analyzed using entropy weighting within a geographic information system (GIS) environment.The exposure factors included hillshade (mean: 94.76), precipitation (mean: 219.78 mm), humidity (mean: 70.97%), and temperature (mean: 2.01 C).Sensitivity factors comprised slope (mean: 7.5), curvature (mean: 0.15), traffic volume (mean: 62073 vehicles), bridge length (mean: 1452.06 m), and tunnel count (mean: 2.33).Adaptive capacity was represented by the density of 119 Emergency Rescue Centers (mean: 0.01).The final vulnerability index had an average of 0.96 with a standard deviation of 0.20.These results provide a practical basis for formulating black ice accident prevention strategies and optimizing the allocation of safety-related resources.The findings also offer valuable policy insights for winter road safety planning and infrastructure vulnerability management.