# Structured Urban Airspace Capacity Analysis: Four Drone Delivery Cases
## 3 Flight Planning Algorithm
### Outer Loop: Multiple sUASs Flight Planning
The total flight time and the total flight distance of thirty sUASs for the FCFS algorithm case is almost same as the LCFS algorithm case. However, the mission completion time for the LCFS algorithm case is 34.5% more efficient than the FCFS algorithm case. Therefore, although the total flight time and the total flight distance are very similar for both cases, the LCFS algorithm case is more efficient with regard to the urban airspace operating time. Both airspace users and airspace service providers prefer to operate as many sUASs as possible in a given time within a given urban airspace. However, if there are more than two urban airspace users with more than two retail points, the urban airspace usage priority will be an essential factor to be considered for the flight planning problems, which is a similar situation reported for today's airspace for commercial aircraft. Advantages of the proposed algorithm are as follows: able to adopt different sequencing algorithms; providing separation-compliant speed profiles that satisfy each sUAS's performance; applicable to various types of route networks; enlarge a solution-searching space by finding a flight route and speed profile of each sUAS simultaneously; and fast computational time. Therefore, we expect that the advantages of Algorithm 1, which provides not only the flight route but also the detailed schedule, will be appropriate to analyse the capacity of the high-density VLL urban airspace populated by many sUASs with short flight times, and many service providers. Figure 6: Iteratively generated airspace graphs. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**sUAS**} & **Retail** & **Service** & \multicolumn{2}{c}{**Waypoint Sequence**} \\
**ID** & **Point** & **Point** & **(Arrival Time Each Waypoint (s))** \\ \hline \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) \\ \hline \(a_{4}\) & E68 & E4 & E68-E24-E17-E1-E2-E3-E4 (180)-(197)-(217)-(244)-(-268)-(280)-(293) 25-25-25-25-25-25 \\ \hline \(a_{5}\) & E68 & E5 & E68-E24-E17-E1-E2-E3-E4-E5 (220)-(237)-(257)-(-284)-(308)-(320)-(333)-(345) 25-25-25-25-25-25 \\ \hline \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) \\ \hline \(a_{22}\) & E68 & E36 & E68-E32-E33-E34-E35-E36 (130)-(147)-(169)-(181)-(193)-(206) 25-25-25-25-25 \\ \hline \(a_{23}\) & E68 & E38 & E68-E32-E33-E34-E35-E36-E37-E38 (170)-(188)-(209)-(221)-(234)-(246)-(258)-(271) 25-25-25-25-25-25-25 \\ \hline \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Flight planning results of 30 sUASs for the 1-to-\(\mathcal{M}\) case (outer loop: FCFS algorithm). \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**sUAS**} & **Retail** & **Service** & **Waypoint Sequence** \\
**ID** & **Point** & **Point** & **(Arrival Time Each Waypoint (s))** \\ \hline \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) \\ \hline \(a_{4}\) & E68 & E4 & E68-E24-E17-E1-E2-E3-E4 (110)-(127)-(147)-(173)-(197)-(210)-(223) 25-25-25-25-25 \\ \hline \(a_{5}\) & E68 & E5 & E68-E24-E17-E1-E2-E3-E4-E5 (70)-(87)-(107)-(133)-(157)-(170)-(183)-(196) 25-25-25-25-25-25 \\ \hline \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) \\ \hline \(a_{22}\) & E68 & E36 & E68-E32-E33-E34-E35-E36 (160)-(177)-(198)-(211)-(223)-(236) 25-25-25-25-25-25 \\ \hline \(a_{23}\) & E68 & E38 & E68-E32-E33-E34-E35-E36-E37-E38 (120)-(137)-(158)-(171)-(183)-(196)-(208)-(221) 25-25-25-25-25-25-25 \\ \hline \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Flight planning results of 30 sUASs for the 1-to-\(\mathcal{M}\) case (outer loop: LCFS algorithm).