## 5 Experimental Analysis
### Preparation of Experimental Data  
The experiment utilizes the actual road network data from a specific district in Jinan. The distribution of road segments and blocks is illustrated in Figure 5 below. The graphical representations in the image are distinguished by various colors that correspond to their respective land use characteristics. As this aspect is not pertinent to the is-sue discussed in this article, no additional explanation will be offered. The numerical designations on the roadways indicate the nodes within the current area, specifically de-noting the intersection points between the roads. Initially, as outlined in Section 2.1, the regional map should be abstracted into a network topology graph. This graph comprises a total of 143 nodes and features 248 edges connecting these nodes. The topological structure is illustrated in Figure 6 below. Figure 5: The current planning map of the distribution area. Before initiating the problem-solving process, it is essential to establish the initial parameters. For the purpose of simplifying calculations, three distribution centers are randomly selected within the designated area. Each distribution center is allocated three distribution trucks, with each truck equipped with a drone to facilitate collaborative distribution. The operational speed of the trucks is denoted in kilometers per hour (km/h), while the drones are capable of flying at a specified speed, also measured in km/h. The operational flight radius of each drone is set at 5 km. In addition, ten nodes within the area are randomly designated as failed nodes. The road sections adjacent to these failed nodes are rendered inoperative, preventing normal passage for the trucks. Consequently, deliveries to these failed nodes must be conducted exclusively by drones. For the initial values utilized in the evolutionary computation, the population size is established at 100 and the number of iterations is set to 200. The weight coefficients are assigned as follows: demand coverage is weighted at 0.5, distribution cost at 0.2, and distribution time at 0.1. To further streamline calculations, the probability of a node experiencing an unexpected event leading to failure is fixed at a constant value of 0.05. The results of these calculations are presented in Figure 7 below. Figure 6: Road network topology diagram. The final distribution paths are presented in Table 4. In this table, the truck's route is delineated by all the points it traverses. In instances of repeated points, delivery occurs upon the first passage, while subsequent crossings are treated as necessary nodes only. The representation of the drone's path is structured as a triplet. The first element denotes the drone's take-off point, the second element indicates the drone's delivery point, and the third element specifies the next target node for the drone. This target node may either be the meeting point with the truck or the subsequent demand point. As illustrated in Figure 7 and Table 4, the spatial distribution of distribution centers, indicated by red five-pointed stars in the figure, demonstrates a well-considered dispersion that facilitates effective coverage of the entire service area. Each distribution center is tasked with servicing its surrounding area, and efficient delivery to demand nodes (represented by blue dots) is accomplished through the deployment of three trucks operating on distinct routes, denoted by solid orange, purple, and green lines, respectively. Concerning the delivery method for invalid nodes (marked in yellow), traditional delivery modes are inadequate, as these nodes are inaccessible to trucks due to road interruptions or other obstacles, leading to service blind spots. To address this issue, the strategy proposed in  
Figure 7: Collaborative path planning map for air–ground distribution. this paper involves the utilization of drone delivery.