## 3 Model Validation and Discussion of the Obtained Results  
Based on the previously presented methods and materials, the proposed approach to nonurban road maintenance management was tested on the real problem of maintaining the regional road network of Herzegovina-Neretva County. Table 5 shows the results of the implementation of the sub-model for determining the weights of the criteria:  
* For Scenario 1, which corresponds to the judgment of nonurban road users, i.e., the population living in areas that connect cities and settlements;
* For Scenario 2, which corresponds to the judgment of representatives of the regional administration;
* For Scenario 3, obtained by the judgment of road and maintenance management experts. The column called average weight represents the compromise solution of all involved stakeholders as the arithmetic mean of all three described scenarios. It can be seen that, according to the compromise solution, the California Bearing Ratio (CBR) is in first place in terms of relative importance as a strength measure of the road subgrade and of the materials used in road construction, while the road level expressed in number of road lines is the least important criterion in determining the maintenance priorities. After implementing the sub-model for determining the weight of the criteria, it is necessary to implement the sub-model for ranking nonurban roads on the regional road network in Herzegovina-Neretva County. Multiplying each element of the normalized matrix \(R=(r_{ij})_{mxn}\) by the relative weight of the criteria (w) gives the weighted normalized decision matrix \(V=(v_{ij})_{mxn}\) and is presented in Table 6. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Criteria** & **Criteria** & **Type of** & **Min/Max** & **Scenario 1** & **Scenario 2** & **Scenario 3** & **Average Weight** \\ \hline C1 & Traffic intensity & Technical & Max & 0.162 & 0.143 & 0.179 & 0.161 \\ \hline C2 & Road length & Technical & Max & 0.035 & 0.030 & 0.043 & 0.036 \\ \hline C3 & Road level & Technical & Max & 0.022 & 0.019 & 0.028 & 0.023 \\ \hline C4 & The social significance & Social & Max & 0.048 & 0.042 & 0.043 & 0.044 \\ \hline C5 & The economic & Social & Max & 0.086 & 0.143 & 0.087 & 0.105 \\ \hline C6 & California Bearing & Technical & Min & 0.212 & 0.176 & 0.218 & 0.202 \\ \hline C7 & Maintenance Cost & Economic & Max & 0.082 & 0.138 & 0.068 & 0.096 \\ \hline C8 & Construction time & Technical & Min & 0.086 & 0.065 & 0.073 & 0.075 \\ \hline C9 & Road width & Technical & Max & 0.027 & 0.024 & 0.048 & 0.033 \\ \hline C10 & Pavement type & Technical & Max & 0.072 & 0.064 & 0.066 & 0.067 \\ \hline C11 &
\begin{tabular}{c} Movement of \\ inappropriately heavy \\ vehicles \\ \end{tabular} & Technical & Max & 0.168 & 0.156 & 0.147 & 0.157 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Averaged criteria weights. Determining of the positive ideal solution (A\({}^{+}\)) and the negative ideal solution (A\({}^{-}\)) and calculating distances between target alternative i and the worst condition (Si\({}^{-}\)) and between target alternative i and the best condition (Si\({}^{+}\)) is given priority rank, expressed as similarity to the worst condition (Qi). According to the obtained rank, the nonurban road labeled as R-418, with the highest value of Qi, has the highest priority in nonurban road maintenance, while the road section marked with R-435a has the best condition of the road with the lowest maintenance priority. Furthermore, the limited annual budget intended for maintenance activities is evenly distributed according to the obtained priority rank, as shown in Table 7.  
Through the implementation of the monitoring and control sub-model, the planned, realized and earned values of maintenance were determined. Based on those values, road maintenance index (MRI), the cost of the remaining percentage of maintenance and the new maintenance cost were determined.