## 3 Results and Discussions
### Quantifying Safety Benefits via Connected Vehicles  
As we venture into a future where connected vehicles (CVs) and intelligent transportation system (ITS) infrastructure are increasingly ubiquitous, the ability to estimate and quantify the safety benefits of such technologies becomes paramount. This study proposes a framework that utilizes advanced technologies to enhance safety management during winter months. The framework relies on the widespread use of CVs and ITS infrastructure to capture snapshots of RSCs, which are then transmitted to a nearby server. The server converts these images into point friction values using a developed friction model, which are then transformed into continuous friction values based on location information. These values are aggregated and converted into collision likelihood ratings, which are relayed to CVs to trigger safety interventions. These interventions include rerouting vehicles if a road segment is identified as dangerous or warning drivers of potential hazards on the road ahead. The process is repeated every few seconds to ensure the most current information. Figure 12 provides a visual representation of this process. This sophisticated system lends to the quantification of safety benefits through crash avoidance estimations. For each 6.5 km road segment, the number of collisions leading to property damage only (PDO), injuries, and fatalities was calculated for the four days where friction values were available. These segments were then evaluated through the binary collision likelihood model to ascertain the probability of a collision. It was assumed that all sections marked as \"collision likely\" had the potential to be avoided in the proposed framework, as the driver would have been prompted to reroute or war  
Figure 11: Decision tree model structure using a 6.5 km segment length. precautions. The resulting model demonstrated labeling accuracy of 80%, identifying 11 road segments as potentially dangerous. These segments were linked with 22 PDO collisions and one injury. In this study, two collision reduction scenarios were examined. The first scenario represented an ideal condition where all vehicles were effectively rerouted before reaching a hazardous road section, leading to a 100% Crash Reduction Factor (CRF). The second scenario applied a more conservative CRF of 18%, drawn from a similar study by the state of California on dynamic real-time road condition warning systems [26]. It is worthwhile highlighting that this 18% CRF was employed for broad estimations of safety benefits when rerouting was unattainable. Table 1 lists the number of collisions reduced and the safety benefits of each scenario. According to Table 1, scenarios one and two would have saved CAD 447,179 and CAD 56,260 over four days, respectively, if the proposed framework had been implemented. It is important to note that these monetary benefits only apply to a small portion of Edmonton's road network; the actual savings should be greater. The above procedure simply demonstrates how the proposed framework can be implemented and how the potential benefits can be quantified. In order to gain a sense of the potential savings on a city-wide scale, traffic collision records for the same four days were obtained. Because these records did not have friction values, they could not be inputted into the developed model to determine whether a warning \begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline
\begin{tabular}{c} CAD \\ 56,260 \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Potential number of collisions reduced over four days within the study area. Figure 12: Proposed framework for implementation in CVs and ITS environments. ing or rerouting would have been triggered. The solution is to use random sampling. Since our model operates at 76.9% accuracy, we can assume that there is a 76.9% probability of our model detecting a dangerous road section.