# Research on Demand-Based Scheduling Scheme of Urban Low-Altitude Logistics UAVs
## 4 Example Verification and Analysis  
### Sample Data and Parameter Settings  
To verify the effectiveness of the model and algorithm in this paper, the actual data of express business volume, distribution points, and demand points in Shanghai are selected as sample data, as shown in Table 1. Select a real distribution point in the region to analyze  
Figure 1: Solution process. the business volume for a certain day. The distribution point has 98 demand points and 5760 packages. According to the regulations on low-altitude use issued by the region, suitable delivery routes for drones have been planned in the preliminary work, that is, two urban air traffic networks constructed using road and building data. When planning the two-layer route, the A* algorithm and 500 \(\times\) 500 grid network are used to avoid the airspace no-fly zone and ground obstacles. The flow chart is shown in Figure 2. The urban air traffic network (RN) constructed using road data is shown in Figure 3, and the urban air traffic network (BN) constructed using building data is shown in Figure 4. The routes on the two floors represent a 100 m high departure route and a 150 m high return route, respectively. The initial delivery time is set at 8:00 a.m. According to constraints (8) and (11), the peak time will start 3 h later and end 5 h later. In order to make the flight of drones during transportation as real as possible, this paper sets the simulation according to the performance parameters of drones currently used in logistics transportation on the market, as shown in Tables 2 and 3. In this article, the idealized situation is considered, and it is stipulated that the drone equipment used in the experiment has been obtained without incurring any other costs due to the purchase of equipment. \begin{table}
\begin{tabular}{c c} \hline \hline
**Demand Point Serial Number** & **Packages to Be Delivered (kg)** \\ \hline B0089 & 68 \\ B0189 & 50 \\ B0268 & 80 \\ B0324 & 69 \\ B0427 & 77 \\... &... \\ B8796 & 58 \\ B9078 & 9 \\ B9111 & 79 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Sample data for express demand forecasting. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline Maximum cargo capacity \(W_{\text{max}}^{m}/\text{kg}\) & 20 & Risk cost of road network per unit distance & 0.4 \\ Maximum flight range & 18 & Risk cost of building network per unit distance & 0.2 \\ \(L_{\text{max}}^{m}/\text{km}\) & 1 & Drone maneuvering cost \(D_{\delta}/\text{Yuan}\cdot\text{sorties}^{-1}\) & 0.5 \\ Maximum flight speed \(v_{\text{max}}/\text{m}\cdot\text{s}^{-1}\) & 10 & Number of drones & 50 \\ Maximum flight altitude limit & 100 & Number of backup batteries & 20 \\ \(H_{\text{min}}^{m}/\text{m}\) & 100 & \(N\) & 20 \\ Minimum flight altitude limit & 150 & Charging duration of drone & 2 \\ \(H_{\text{max}}^{m}/\text{m}\) & 150 & \(h\) & 2 \\ Standard time-cost coefficient & Working hours of drone & \\ \(T_{\text{so}}/\text{Yuan}\cdot\text{min}^{-1}\) & 0.1 & \(T_{\text{m}}/\text{min}\) & 30 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Initial model simulation data. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline \(k_{1}\) & \(\frac{T_{\text{so}}}{\sigma^{2}-e^{2}}\) & \(b_{1}\) & \(\frac{T_{\text{so}}(e^{2}-2e^{2})}{e^{2}-e^{2}}\) \\ \(k_{2}\) & \(\frac{T_{\text{so}}}{\sigma^{2}-e^{2}}\) & \(b_{2}\) & \(\frac{T_{\text{so}}(e^{2}-2e^{2})}{e^{2}-e^{2}}\) \\ \(k_{3}\) & \(\frac{D_{\deltaFigure 3: Urban air traffic network constructed by road data. Figure 2: Flow Chart of Urban Air Traffic Network Path Planning. From Table 5, it can be seen that, during the RN route, drone 34 has the highest number of deliveries, at 17. Although drone 21 has been delivered 16 times, the delivery was completed the latest. It is worth noting that drone 21 needs to be charged at this time, so the total delivery time is 5 h and 36 min.