## 4 Conclusions  
The provision of real-time winter road surface condition (RSC) information has the potential to improve road safety by allowing road users to take the necessary precautions needed on hazardous roads or avoid them altogether. However, because RSCs are generally provided in the form of qualitative descriptors such as bare, partially snow-covered, and fully snow-covered, road users are forced to make the safety interpretations themselves. This is especially problematic for intermediary classes like partially snow-covered that cover a wide range of conditions. A more straightforward way to present RSC information is to provide collision likelihood directly, thus removing the need for user interpretation. To achieve this, we established a framework for carrying out this conversion process, which involved a friction interpolator and a binary collision likelihood model. The novelty of this approach lies in the fact that most existing friction-based collision model studies focus solely on dry and wet conditions and do not consider segment length as a tunable parameter. This research stands apart as one of the few studies that have examined friction's effect on winter collisions, provided a framework for calibrating segment length, and quantifies safety benefits in a connected vehicle (CV) environment. Key research findings are summarized below:  
* Firstly, we developed an interpolator to convert point friction values into continuous values. In order to identify the most accurate interpolators, six interpolators were evaluated using datasets with increasing distance between measurements (from 0.1 to 1 km): ordinary kriging (OK), regression kriging (OK), RandomForest (RF), the RandomForest spatial interpolator (RFSI), and hybrid models (RFOK and RFSIOK). The results show that OK had the lowest error and was the least affected by increased separation distance. * Next, after demonstrating that continuous friction values could be accurately generated, we developed binary collision models using segment lengths from 500 m to 20 km. Based on model accuracy and intuitiveness, the most optimal model was found at 6.5 km, which used the parameters friction and AADT and exhibited classification accuracy of 76.9%. * Finally, we used the proposed framework to generate advance warnings for road users, which, if implemented in an environment with an ITS and CVs, offers significant safety benefits through collision avoidance. In a scenario where all vehicles were effectively rerouted before reaching a hazardous road section, a 100% Crash Reduction Factor (CRF) was achieved, leading to estimated savings of CAD 7,796,420 over four days. Even in a more conservative scenario with an 18% CRF, the estimated savings were CAD 1,141,726 over the same period, showcasing the tangible impact of this framework. In terms of study limitations, the most prominent was the shortage of data. The number of data points available for interpolator development may have resulted in the machine learning models being underfitted, which is especially true when the separation distance between measurements increased to distances like 1 km, where only seven observations were available. Therefore, the performance of RF and the RFSI could be constrained by dataset size. In addition, dataset size was also an issue for the collision model, as it was developed with only four days of data because these were the only four days where friction data were available. Further research should concentrate on expanding the size of the dataset used. As mentioned, the performance gap between RF and OK could be attributed to the underfitting of the RF model; therefore, it would be beneficial to conduct the comparison on a more extended road segment where more data points are available. Additionally, more collision data are required to create a more accurate collision model since only four days of data are currently utilized.