## 5 Results and Discussion
Each number has three fuzzy values (\(l\), \(m\), \(u\)), as shown in Table 6, using Equation (2) and then these fuzzy numbers used in the fuzzy normalized decision matrix as shown in Table 7. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sr. No** & **Demographic** & **Categories** & **Frequency** & **Percentage (\%)** \\ \hline \multirow{2}{*}{\(1\)} & \multirow{2}{*}{Gender} & Female & 54 & 51.5 \\  & & Male & 51 & 48.5 \\ \hline \multirow{2}{*}{\(2\)} & Participation in the & Yes & 105 & 93.4 \\  & survey & No & 7 & 6.6 \\ \hline \multirow{4}{*}{\(3\)} & \multirow{4}{*}{Age} & 18–24 & 23 & 21.90 \\  & & 25–34 & 24 & 22.86 \\  & & 35–44 & 28 & 26.67 \\  & & 45–54 & 20 & 19.05 \\  & & 55–64 & 10 & 9.52 \\ \hline \multirow{4}{*}{\(4\)} & \multirow{4}{*}{Education} & Less & 20 & 19.05 \\  & & Secondary & 40 & 38.10 \\  & & Diploma & 5 & 4.76 \\  & & Bachelor & 36 & 34.28 \\  & & Master & 3 & 2.86 \\  & & PHD & 1 & 0.95 \\ \hline \multirow{2}{*}{\(5\)} & \multirow{2}{*}{Disabled persons’ vehicles used} & Powered wheelchair & 37 & 35.24 \\  & & Manual wheelchair & 62 & 59.05 \\ \cline{1-1}  & & Artificial limbs & 6 & 5.71 \\ \hline \multirow{2}{*}{\(6\)} & Independence mobility & Yes & 78 & 74.29 \\ \cline{1-1}  & Outside a residence & No & 27 & 25.71 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Descriptive statistics of the data. In Step 2, the estimated weights were multiplied by the fuzzified and normalized route parameters in Table 5 to calculate the normalized fuzzy weighted decision matrix by multiplying the weights of the criteria \(\widetilde{w}_{j}\) by the fuzzified normalized scores \(\widetilde{x}_{ij}\) by using Equation (5b). For instance, the calculation for the first participation for route R1 is as follows:  
For length:  
\[(0.05\times 1,\ 0.1\times 1,\ 0.17\times 3)=(0.050,\ 0.100,\ 0.510)\]  
Number of junctions:  
\[(0.32\times 5,\ 0.4\times 7,\ 0.5\times 9)=(1.600,\ 2.800,\ 4.500)\] \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Rank (K)**} & \multicolumn{4}{c}{**Fuzzified Values**} \\ \cline{2-5}  & \(I\) & \(m\) & \(u\) \\ \hline
1 & 0.32 & 0.40 & 0.50 \\
2 & 0.23 & 0.30 & 0.39 \\
3 & 0.14 & 0.20 & 0.28 \\
4 & 0.05 & 0.10 & 0.17 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Fuzzy rank sum weights. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Linguistic Scale**} & \multicolumn{2}{c}{**Fuzzified Value**} & \multicolumn{2}{c}{**Absence of**} & \multirow{2}{*}{**Length (m)**} & \multirow{2}{*}{**Number of**} & \multirow{2}{*}{**Slope (\%)**} \\ \cline{2-5}  & \(I\) & \(m\) & \(u\) & & & & \\ \hline Very low (VL) & 1 & 1 & 3 & \(>\)75 & \(>\)800 & \(\geq\)4 & \(>\)6 \\ Low (L) & 1 & 3 & 5 & \(>\)50–75 & \(>\)600–800 & 3 & \(>\)4–6 \\ Medium (M) & 3 & 5 & 7 & \(>\)25–50 & \(>\)400–600 & 2 & \(>\)2–4 \\ High (H) & 5 & 7 & 9 & \(>\)0–25 & \(>\)200–400 & 1 & \(>\)0–2 \\ Very high (VH) & 9 & 10 & 10 & 0 & 0–200 & _No_ & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Linguistically defined fuzzy scale for the criteria. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{**City**} & \multicolumn{2}{c}{**Length**} & \multicolumn{2}{c}{**Number of Junctions**} & \multicolumn{2}{c}{**Absence of Footpath**} & \multicolumn{1}{c}{**Slope**} \\ \hline \multirow{3}{*}{Riyadh} & R1 & Very low & High & High & Very low \\  & R2 & Very low & Medium & High & Very low \\  & R3 & Medium & Medium & High & Very low \\ \hline \multirow{3}{*}{Qassim} & R4 & Low & High & High & Very low \\  & R5 & Very low & Very High & High & Medium \\  & R6 & Very low & Medium & High & Medium \\ \hline \multirow{3}{*}{Hail} & R7 & Low & Medium & Low & Very low \\  & R8 & Very low & Medium & Medium & Very low \\ \cline{1-1}  & R9 & Very low & Very low & Medium & Low \\ \cline{1-1}  & R10 & High & Medium & Medium & Medium \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Linguistic scale of the ratings of the routes.