## 3 Methodology
### Station Demand  
The process of determining the necessary quantity of EVCSs utilizes a thorough and multi-step technique, which is outlined in Figure 3. The results of the poll are combined with demographic information and statistical modeling to create well-informed predictions about EV adoption and the need for EVCSs. The findings can help with infrastructure development, urban planning, and policy choices around EV charging stations and sustainable mobility. The methodology for calculating the number of EVCSs in Hail City involves a multi-step process. It begins with measuring the area of each city zone using Google Earth (version 7.3.6.9345). Population data, combined with survey results on family size, car ownership, and willingness to convert to EVs, inform the number of cars per \begin{table}
\begin{tabular}{l l l} \hline \hline
**Sub-District** & **Area (ha)** & **Average Population (\%) [44]** \\ \hline
1. Al-Muntazah & 713 & 1.58 \\ \hline
2. Aja & 328 & 0.48 \\ \hline
3. Al-Masyaf & 502 & 0.74 \\ \hline
4. Al-Zahraa + Alkhazami + Alyasmeen & 1544 & 22.82 \\ \hline
5. Al-Mamlaka & 131 & 0.34 \\ \hline
6. Al-Samraa + Az Zibarah + Al-Muzaabar & 926 & 20.52 \\ \hline
7. Al-Wosaytaa + Almahata & 966 & 23.20 \\ \hline
8. Al-Hanifah & 1105 & 14.29 \\ \hline
9. Al-Khamashya & 542 & 5.01 \\ \hline
10. Al-Shebily & 229 & 0.25 \\ \hline
11. Al-Naqra & 1070 & 1.98 \\ \hline
12. Al-Rasf & 521 & 0.58 \\ \hline
13. Al-shefaa & 597 & 2.21 \\ \hline
14. Soq Al-Mashya & 228 & 1.68 \\ \hline
15. Al-Wadi & 639 & 1.18 \\ \hline
16. [PERSON] & 681 & 0.50 \\ \hline
17. [PERSON] & 716 & 2.65 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Hail sub-region area and population. Figure 2: Hail sub-regions. The numbers in the figure shown in the Table 2.  
zone. The daily battery consumption of EVs is estimated based on average trip lengths and consumption rates. The capacity of charging stations and their ability to serve multiple cars per day are factored in. Finally, the total number of charging stations required is calculated, considering station maintenance and downtime. This approach integrates geographical, demographic, and behavioral data to provide a comprehensive infrastructure estimate for sustainable transport. The geographic area (A) is determined utilizing Google Earth to comprehend the physical domain upon which the ensuing analysis is executed. The average population density (D) is procured through a statistical report [44]. The total population (P) is calculated as demonstrated in Equation (1):  
\[P=A\times D \tag{1}\]  
Figure 3: Scheme for performing the number of charging stations calculation. The average number of family members (M) is calculated utilizing a weighted average, derived from the questionnaire responses concerning family size. The weighted average equation used is as follows:  
\[M=\frac{\sum_{i=1}^{N}(x_{i}\times w_{i})}{\sum_{i=1}^{N}(w_{i})} \tag{2}\]  
where:  
\(\mathrm{x_{i}}\) = Number of family members in the category,  
\(\mathrm{w_{i}}\) = Percentage of families in category,  
N = Total number of categories. The total number of families (F) is derived by dividing the total population (P) by the average number of family members (M):  
\[F=\frac{P}{M} \tag{3}\]  
The average car ownership per family (O) is computed by utilizing a weighted average extracted from the survey data regarding car ownership. The utilized Equation (4) is calculated as demonstrated below:  
\[O=\frac{\sum_{i=1}^{m}(y_{i}\times v_{i})}{\sum_{i=1}^{m}(v_{i})} \tag{4}\]  
where:  
\(\mathrm{y_{j}}\) = Number of cars in the ownership category,  
\(\mathrm{v_{j}}\) = Percentage of respondents in the ownership category,  
\(\mathrm{m}\) = Total number of ownership categories. The total number of cars (C) is determined as:  
\[C=O\times F \tag{5}\]  
The average propensity toward converting to EVs (T) is extracted from the survey data, representing the proportion of respondents indicating a shift toward EV usage.