Spatial Mapping of Winter Road Surface Conditions via Hybrid Geostatistical Techniques

Abstract

In recent decades, road weather information systems (RWISs) have gained in popularity with road maintenance authorities. However, RWIS stations only provide point measurements that are often unrepresentative of distant surrounding areas. To address such limitations, this study employs a hybrid geostatistical interpolation method, regression kriging (RK), to fill in the large spatial gaps at unmonitored locations. Road surface temperature (RST) data collected by an automated vehicle system along selected interstate highways were used to model the RST spatial variation patterns via semivariograms, which were then used to interpolate the conditions in between RWIS stations. Cross-validation results indicated that RK successfully captured the spatial variation of RST along the highway segment. The nugget-to-sill ratio obtained from semivariograms was further utilized to characterize the weather events, and the results implied that stronger winds and heavier rainfalls were likely to form a stronger spatial dependence within RST. The findings of this research contribute to better understanding of the influences of meteorological factors in RST as well as improved models for inferring the road surface conditions between RWIS stations.