Optimizing RWIS Locations with Wasserstein Distance and Geostatistics: A Case Study in South Korea

Road Weather Information Systems (RWISs) are essential components of modern Intelligent Transportation Systems (ITSs) deployed in cold regions to gather real-time data on winter weather and road surface conditions. Despite their benefits, the high cost associated with RWIS installations demands optimized placement strategies to maximize their utility and cost-effectiveness. Geostatistics-based RWIS location-allocation methods, particularly those involving semivariogram modeling to quantify underlying spatial characteristics, have gained international recognition. However, new locations require unique semivariogram models, a process that is time-consuming and constrained by the availability of comprehensive datasets, often rendering location analysis challenging or infeasible. Addressing these limitations, this study introduces an innovative approach using Wasserstein Distance (WD) to link semivariograms across different datasets. This method streamlines optimization by eliminating the need for repetitive semivariogram modeling in new study areas. Our findings demonstrate that WD-matched models replicate the location choices of original models with a high degree of similarity while ensuring that clean-slate locations remain proximate to those of original models, enhancing geographic equity in RWIS deployment. This validates the practicality of reusing developed semivariogram parameters for WD-matched highways, significantly reducing the need for new geostatistical analyses and enhancing the framework’s applicability and accessibility for RWIS deployment across diverse geographic regions.