# Determining Factors Influencing Operating Speeds on Road Tangents
## 3 Results  
### Single-Carriageway Road Tangents  
For the single-carriageway road tangents, the statistical analysis focused on examining the relationships between the operating speeds (V85) and various factors, both numerical and categorical. The approach varied depending on the type of variable being analyzed (Table 2). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Factors—Single-Carriageway Roads** & **Type** & **Normal Distribution** & **Test Used** \\ \hline Passenger side & Categorical—Multi-level & — & ANOVA \\ Shoulder type & Categorical—Multi-level & — & ANOVA \\ Edge line quality & Categorical—Multi-level & — & ANOVA \\ Visibility & Categorical—Multi-level & — & ANOVA \\ Pavement quality & Categorical—Multi-level & — & ANOVA \\ Lateral access density & Continuous & NO & Spearman \\ Tangent length (up to measurement & Continuous & NO & Spearman \\ point) & Continuous & NO & Spearman \\ Tangent length (from measurement & Continuous & NO & Spearman \\ point) & Speed limit & Categorical—Multi-level & — & ANOVA \\ AADT & Continuous & NO & Spearman \\ ASDT & Continuous & NO & Spearman \\ Lane width & Categorical—Multi-level & — & ANOVA \\ Longitudinal slope & Continuous & YES & Pearson \\ Crash ratio & Continuous & NO & Spearman \\ Design speed & Categorical—Multi-level & — & ANOVA \\ Terrain type & Categorical—Multi-level & — & ANOVA \\ Guardrail presence & Binary & — & \(t\)-test \\ Shoulder lane presence & Binary & — & \(t\)-test \\ Radius of previous curve & Continuous & NO & Spearman \\ Radius of following curve & Continuous & NO & Spearman \\ Road category & Binary & — & \(t\)-test \\ Overtaking allowed & Binary & — & \(t\)-test \\ Heavy goods vehicles share & Continuous & NO & Spearman \\ Disruptive factor presence & Binary & — & \(t\)-test \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Statistical approach for factors on single-carriageway road tangents. For numerical factors, the normality of the data distribution was first assessed using the Shapiro-Wilk test. This step was essential to determine whether the data met the assumption of normality. Continuous variables that were found to be normally distributed were analyzed using the Pearson correlation, while those that did not meet the assumption of normality were analyzed using [PERSON]'s rank correlation. The detailed normality distribution (Shapiro-Wilk test) results are shown in Figure 10.  
Based on the results of the Shapiro-Wilk test for normality, further analysis was conducted to assess the relationships between the operating vehicle speed and continuous factors. For one factor--longitudinal slope--which was found to be normally distributed, the Pearson correlation was applied to evaluate the strength and direction of the relationship with operating speed. For the remaining continuous factors, which did not meet the assumption of normality, [PERSON]'s rank correlation was used to assess the monotonic relationships. The results of both the Pearson and Spearman are shown in Table 3.  
For the binary categorical factors, a \(t\)-test was conducted to test for the significant differences in operating speeds between the two categories. The results of the \(t\)-test analysis are shown in Figure 11.  
Figure 10: Shapiro–Wilk test for continuous factors—single-carriageway road tangents (Values marked with * indicate statistically significant results at the 0.05 level). For multi-level categorical variables the relationship between the operating speeds and the different levels within each variable was analyzed using an analysis of variance (ANOVA). This approach allowed for the assessment of significant differences in operating speeds across multiple categories. The ANOVA results are shown in Figure 12.