# Determining the Safety Level of State Roads: An Italian Case Study
## 6 Results and Discussion
### Comparison of Safety Indicators  
By comparing the safety rankings of \(1\) km of elementary sections according to different safety performance indicators, it is possible to observe the influence of the methods. The analysis encompasses 132 elementary sections of 1 km each, evaluating the excess of expected crashes over predicted crashes (Figure 10) alongside SAPO (Figure 10). Both indicators exhibit a similar overall trend, with peaks aligning at corresponding road sections. However, the most critical sections according to the excess of crashes, characterized by  
Figure 8: Example of graphical representation of safety ranking according to: (**a**) Observed crash rate; (**b**) expected crash rate. Figure 7: Example of graphical representation of safety ranking according to: (**a**) Observed crash frequency; (**b**) expected crash frequency. Figure 9: Example of graphical representation of safety ranking according to: (**a**) Excess of expected crashes over predicted crashes; (**b**) SAPO. positive values of the indicator (60 elementary sections), are more than the sections with positive values according to the SAPO (18 elementary sections). It is worth noting that all 18 sections considered dangerous by SAPO are included in the 60 critical sections identified by the excess of crashes. In addition, SAPO displays distinct peaks correlating with instances of road fatalities, confirming the high sensitivity of the indicator to this aspect. The 60 most dangerous sections, according to the excess of accidents, are then analyzed in more detail to understand if the safety ranking is comparable to that obtained with SAPO. Since SAPO only classified 18 sections as dangerous, the remaining 42 sections classified as critical by the excess of crashes would be neglected. Thus, the SAPO is computed again (as detailed in Equation (9)) according to the local base value of accident cost density (\(BTCI_{local}\)) and not based on the average Italian base value defined by the Italian guidelines (\(BTCI\)) [5]. \[SAPO_{local}=\frac{N_{m}\cdot C_{m}+N_{f}\cdot C_{f}}{L}-(BTCI_{local}\cdot 36 5\cdot AADT)/10^{6} \tag{9}\]  
The local base value of accident cost density (\(BTCI_{local}\)) is computed according to average annual daily traffic (\(AADT\)), the overall length of the analyzed road section (\(L\)), and the actual number of road fatalities (\(N_{m}\)) and injuries (\(N_{f}\)) that occurred on the analyzed road section. The obtained value for the present case study is equal to 11.70 \(\upxi/(1000\cdot\text{veic}\cdot\text{km})\). Since this value is lower than the suggested national average, the road in question is generally less dangerous than the average proposed by the Italian guidelines [5] for Italian rural roads. For this reason, the local SAPO may classify some sections as dangerous that were excluded by the global SAPO. Figure 10: Safety performance indicator values for all 132 elementary sections: (**a**) Excess of expected crashes over predicted crashes; (**b**) SAPO. It is then possible to compare the excess of crashes with the global SAPO and the local SAPO for the 60 most critical road sections, as represented by Figure 11, where elementary sections are sorted in ascending order by excess of crashes. Among the 60 sections with a positive excess of crashes, 18 have a positive value for global SAPO and 39 have a positive value for local SAPO. Therefore, the local SAPO exhibits a better capacity to identify critical road sections compared to the global SAPO. Peaks in SAPO values (exceeding 400 k\(\epsilon\)/km\(\cdot\)year) correlate with incidents involving road fatalities. Figure 12a,b illustrate the correlation between the excess of crashes and both global SAPO and local SAPO, respectively. Local SAPO shows a better correlation with the excess of crashes, likely due to its ability to account for specific study context characteristics rather than relying on the average cost density in Italy.