## 6 Discussion  
The conceptual model described the elements, factors, and aspects considered and provided guidance for the design, management, and improvement of mobility networks, vertiports, and aircraft units. The simulation model and the design methodology provided a step-by-step methodology that could be applied to any area and mobility system to optimize overall system performance and improve customer experiences and satisfaction. In this case, it was applied for electric aviation to show the considerable optimization  
\begin{table}
\begin{tabular}{c c c c} \hline
**No.** & **Indicator** & **High Performance** & **Low Performance** \\ \hline
1 & Passengers (persons) & 6888 & 6720 \\
2 & Average aircraft on ground (\%) & 81.99 & 82.43 \\
3 & Average aircraft availability (\%) & 98.7 & 72.2 \\
4 & Average aircraft utilization rate (\%) & 18.01 & 17.57 \\
5 & Average flight request backlog (requests) & 6.1 & 8.2 \\
6 & Average flight request backlog (minutes) & 8.3 & 10.3 \\
7 & Average capacity utilization rate of vertiports (\%) & 43 & 50 \\
8 & Average vertiports full (\%) & 14.7 & 20.3 \\
9 & Average vertiports empty (\%) & 14.1 & 11.8 \\
10 & Average arrival backlog (aircraft units) & 10.4 & 12.5 \\
11 & Average arrival backlog (minutes) & 5.8 & 7.6 \\ \hline \end{tabular}
\end{table}
Table 2: Simulation results for maintenance factor in a high demand level scenario.  
\begin{table}
\begin{tabular}{c c c} \hline
**No.** & **Indicator** & **Redistribution Model** & **No Redistribution Model** \\ \hline
1 & Passengers (persons) & 6888 & 6773 \\
2 & Average aircraft on ground (\%) & 81.99 & 82.29 \\
3 & Average aircraft availability (\%) & 98.7 & 98.7 \\
4 & Average aircraft utilization rate (\%) & 18.01 & 17.71 \\
5 & Average flight request backlog (requests) & 6.1 & 25.7 \\
6 & Average flight request backlog (minutes) & 8.3 & 12.9 \\
7 & Average capacity utilization rate of vertiports (\%) & 43 & 42 \\
8 & Average vertiports full (\%) & 14.7 & 28.3 \\
9 & Average vertiports empty (\%) & 14.1 & 28.4 \\
10 & Average arrival backlog (aircraft units) & 10.4 & 47.9 \\
11 & Average arrival backlog (minutes) & 5.8 & 9.9 \\ \hline \end{tabular}
\end{table}
Table 3: Simulation results for redistribution factor in a high demand level scenario.  
potential. Using this tool, capacity bottlenecks due to excess aircraft units were reduced, and its accuracy may increase once real-world data can be obtained. The simulation provided an overview of the vertiports and the aircraft units needed to reach a certain service level for the population of a regional area.  
There will always be challenges to the system's capacity, wherein bookings exceed available flights or unexpected circumstances impact the overall efficiency. For those very likely scenarios, the following measures along with a digital twin model should be considered:  
* The real-time monitoring of vertiports with their capacity. Allowing users of smart-device apps to view where and what options are available for on-demand transport. Short-term reservations could be an option, such as within 30-min time frames.
* An app could also connect users and allow them to meet and transfer seats and improve the utilization rate of the e-aircraft while minimizing the use of the capacities at the stations and reducing wait times.
* A team could be allocated to redistribute aircraft to meet on-demand bookings and prevent overcrowding at vertiports.
* Additional services could be provided, such as a business option for those who need on-demand transportation and can afford to pay extra for customized processing such as longer reservation windows or long-term bookings.