## 1 Introduction
These models were evaluated using mud sea content as the target variable, among which RF + OK, RF, and RF + IDW produced the lowest errors. [PERSON] et Al. [7] also evaluated the performance of RF against RK and OK in the interpolation of solar flare radiation. The results generated also showed RF as the best interpolator, producing lower errors than the two geostatistical interpolators. Recognizing the strength of RF, [PERSON] et al. [8] created a modified RF model called the RandomForest spatial interpolator (RFSI). The difference between this and traditional RF is that two additional covariates were added: \"values at nearby locations\" and \"distance to these measurements\". The reasoning is that these two features allow RF to learn the similarity between neighboring values. Using precipitation and temperature as the target variables, the author compared the performance of RFSI with RF, RK, and IDW. Overall, it was found that the RFSI was the superior interpolator by a small margin. In the field of collision modeling with friction as a parameter, studies generally do not focus on winter conditions. [PERSON] et al. [4] performed one of the few studies that used friction coefficients as a predictor of winter collision frequency. In their study, weather and maintenance variables, road surface friction, and road and traffic characteristics were assumed to be predictors of collision frequency. Using structural equation modeling (SEM), the authors found that friction has a direct causal relationship with collision frequency; collisions decrease when friction increases. On the other hand, maintenance operations and weather data affect collision frequency through friction as a medium, i.e., maintenance operations and weather are a predictor of friction, which is a predictor of collisions. [PERSON] et al. [9] also used friction for collision modeling based on year-round data; however, the target variable was collision severity instead of collision frequency. Other variables like roadway, traffic, and driver characteristics were included as predictors. A total of four models were evaluated: the logit model, an SVM, an artificial neural network (ANN),and XGBoost, among which logit, the ANN, and XGBoost had near-identical accuracy of around 70%. Studies have also attempted to formulate the relationship between collisions and friction through proportional analysis and safety performance functions (SPFs), which all appear to center around wet versus dry conditions. It has been found that sites linked to collisions had a lower mean friction value than the randomly selected ones [10]. Regarding SPFs, the coefficient of friction is always negative, signifying an inverse proportional relationship between friction and collision frequency [11; 12]. Overall, it is evident that there are research gaps in both interpolator development and collision modeling. Previous studies on road weather interpolation have focused on geostatistical methods. Although ML and hybrid methods have shown superior performance as interpolators in environmental studies, their performance on road weather variables has yet to be evaluated. For collision modeling, although there appears to be a strong relationship between friction and collision occurrence, only one study has explored this relationship concerning winter conditions. Therefore, further research is needed to confirm the validity of the findings and to determine the predictive accuracy of a friction-based collision model. In addition to the gaps mentioned above, there is another shortcoming that affects both research areas. Previous studies on friction-based collision modeling have ignored the importance of choosing the right segment length, which affects the balance of collision and non-collision segments in the training data. To find the optimal length, interpolators are needed to generate continuous friction values to ensure that the friction values assigned to each segment are representative.