## 1 Introduction  
Jurisdictions in North America currently provide winter road surface conditions (RSCs) on their 511 websites. Ideally, the information provided will allow road users to avoid dangerous roads and take precautions on potentially hazardous segments that cannot be avoided. In reality, the usability of this information is rather limited. Most commonly, RSC information is provided in the form of qualitative descriptions. For example, Alberta, CA, and Iowa, USA, use a three-category system of bare, partially snow-covered, and fully snow-covered to describe the condition of the road surface [1, 2]. The problem with this kind of system is that there are cases where a single category simultaneously represents two conflicting safety levels. Such is the case with the partially snow-covered class mentioned above, where the amount of grip provided varies over a wide range [3]. When road users are confronted with this type of ambiguous condition, it becomes unclear what the appropriate response should be. This problem can be solved by changing the way the information is presented. Instead of providing RSC information, collision likelihood is provided instead. The advantage of this change is that road users no longer need to make their own safety interpretations, as potentially unsafe roads are identified for them. However, for this transition to be possible, it is recommended that friction values be used in place of qualitative descriptions due to the relationship between friction and collisions [4]. Changing the surrogate measure used to represent RSCs is only half the problem; the spatial coverage of RSCs also needs to be extended. From our observation, the RSC of a road is assumed to be constant over long distances. This assumption may be incorrect according to the existing literature, where it has been found that conditions tend to vary even in short stretches [5]. Therefore, the actual spatial coverage of the information provided is somewhat limited. This lack of condition information makes collision occurrence modeling incredibly difficult. The reason is that road length is a tuned parameter based on collision data. The goal is to find an optimal length that contains a balanced number of segments with and without collisions, which can only be achieved with dense friction measurements so that aggregation can be performed at all lengths. The same can be said for model implementation, where continuous friction values ensure that the friction value assigned to each road segment is representative. One way to solve this spatial coverage problem is through interpolators, which can fill in the gaps with missing data. Researchers in the past have performed studies that examined one aspect of either interpolar development or collision modeling, but never both. In the area of developing interpolators, the majority of the studies that involve road weather-related variables implemented geostatistical methods. An example of this kind of study was performed by [PERSON] et al. [3], where they attempted to interpolate road surface temperature (RST) and road surface index--a surrogate friction measure--using regression kriging (RK). The results showed that the interpolated RST values could have RMSE values as low as 0.237, and interpolating RSI resulted in an RMSE of 0.15. Other than providing accurate estimations, the authors also found RK to be able to mimic the measured spatial structure closely. Another similar study was conducted by [PERSON] et al. [5], who also identified RK as a high-performing interpolator for RST and RSI. Outside of road weather-related research, there has been a growing interest in using machine learning (ML) for interpolations. Intending to evaluate ML models as an interpolator, [PERSON] et al. [6] compared the performance of RandomForest (RF), support vector machines (SVMs), regression trees, ordinary kriging (OK), inverse distance weighting (IDW), and hybrid models that combined two algorithms.