# An Application of Android Sensors and Google Earth
## 7 Priority Setting Procedure  
Different types of priority methods are commonly used. These models range from simple subjective rankings to true optimization. It was decided, however, to use a parameter-based priority ranking method. This method is relatively simple and aims to answer the basic questions of where to apply maintenance and in which order. For this purpose, the dependable factors in the decision analysis should be specified. In this study, the selected factors were roughness, traffic, road class, special factors (to emphasize the importance of the selected roads), and the maintenance budget. The simple priority method evaluates the following decision form which has m alternatives (road segments) associated with n attributes (dependable factors):  
\[\begin{array}{ccccc}A_{1}&\begin{array}{ccccc}B_{1}&\dots&B_{j}&\dots&B_{n }\\ \vdots&\begin{array}{ccccc}x_{11}&\dots&x_{1j}&\dots&x_{1n}\\ \vdots&\dots&\vdots&\dots&\vdots\\ x_{11}&\dots&x_{ij}&\dots&x_{in}\\ \vdots&\dots&\vdots&\dots&\vdots\\ x_{m1}&\dots&x_{11}&\dots&x_{mm}\end{array}\\ \end{array}\]  
where:  
\(B_{j}\) = the \(j\)th alternative (road segment).  
\(A_{i}\) = the \(i\)th attribute (dependable factor).  
For decision-making issues, the priority method usually uses subjective weights defined by the decision-makers. In this situation, a set of weights \(W\) = (\(w_{1}\), \(w_{2}\),..., \(w_{n}\)), \(\Sigma w\) =1, was determined by the decision-makers. One of the techniques for determining objective weights is the Shannon entropy method [17].  
The calculation process for the Shannon entropy method is below:  
1. Construct the normalized decision \(r_{i}\), \[r_{i}=\frac{x_{i}}{\sum_{i=1}^{m}x_{i}}\]
2. Construct the vector of the Shannon entropy \(e\) = (\(e_{1}\), \(e_{2}\),..., \(e_{j}\),..., \(e_{n}\)), \[e_{j}=-\frac{1}{\ln m}\sum_{i=1}^{m}r_{i}\ln r_{i}\]  
Figure 10: The maintenance cost for selected segments.  
3) Calculate the vector of diversification degrees \(d=(d_{1},d_{2},\ldots,d_{j},\ldots,d_{n})\), \[d_{j}=1-e_{j}\] 4) Calculate the vector of criteria weights \(W=(w_{1},w_{2},\ldots,w_{j},\ldots,w_{n})\), \[w_{j}=\frac{d_{j}}{\sum_{j=1}^{n}d_{j}}\] (1)  
For this study, the weight calculation process was performed using the Shannon entropy method and MS Excel software. Table 4 shows the weight factors for the prioritization.  
The following model determined the maintenance priority for each segment:  
\[PI=\ \frac{(w_{IRI}*IRI+w_{TF}*TF+w_{FC}*FC+w_{SR}*SR-w_{MF}*MF)}{5}\]  
where:  
\(PI=\) priority index.  
\(TF=\) traffic factor.  
\(FC=\) road classification factor.  
\(SR=\) special factor to emphasize the importance of selected roads.  
\(MF=\) maintenance budget factor.  
To decide on the specific segments that should be implemented first, the road segments were sorted according to their priority index in descending order. Figure 11 presents the results of the calculated priority indexes of segments in descending order. As can be seen, the (6/100) road section had the highest priority index score, with 0.98, 0.99, 0.98, 0.97, and 0.32 for the traffic factor, road classification factor, special factor, roughness, and budget, respectively. The (1/140) road segment, on the other hand, had the lowest priority index score, with 0.33, 0.66, 0.33, 0.19, and 0.41 for the traffic factor, road classification factor, special factor, roughness, and budget, respectively.  
\begin{table}
\begin{tabular}{c c c} \hline
**Factor** & **Weights** & **Mark** \\ \hline Traffic factor weight (\(w_{TF}\)) & 0.127 & + \\ \hline Road class factor weight (\(w_{PC}\)) & 0.065 & + \\ \hline Special factor weight (\(w_{SR}\)) & 0.111 & + \\ \hline Roughness weight (\(w_{IRI}\)) & 0.199 & + \\ \hline Maintenance budget weight (\(w_{MF}\)) & 0.498 & \(-\) \\ \hline \end{tabular}
\end{table}
Table 4: Weight factors for prioritization.