*4.2. UAV Safety Improvement by the Zoning Approach*

Given the UAV flight trajectories obtained from simulation runs, we compute the distances between two operating UAVs at each time stamp to measure the proximity between UAVs. Given that a key performance measurement from the safety perspective would be the separation level between UAV flights, we compute the 5th percentile of the proximity between UAVs (i.e., the distance between two flying UAVs) to identify the minimum distance gap guaranteed for the majority of the UAV flights. Figure 3 shows the distribution of the 5th percentile proximity values for 20 simulation runs under the four different problem classes and the three different UAV deployment strategies (zoning vs. closest vs. closest with thresholds).

From the figure, it is observed that for all the problem classes, the zoning approach shows greater distance gaps than the other considered strategies. While the distance gap increases when the demand distribution C is applied (which seems inevitable due to the density of the demand nodes in the dataset), the zoning approach can still provide greater distance gaps between UAVs than the other strategies. Given that collision avoidance action is taken when a UAV detects an object within a certain range, a greater distance between UAVs clearly indicates that UAVs are likely to be cleared with less efforts for collision avoidance from both UAV operators and UTMs. The safety improvement by the zoning approach becomes clearer in Figure 4, where distributions of the closest distance between two operating UAVs from the experiments are presented.

**Figure 3.** The 5th percentile of the distances between operating UAVs: (**a**) demand distribution U; (**b**) demand distribution C.

**Figure 4.** The worst case proximity between operating UAVs: (**a**) demand distribution U; (**b**) demand distribution C.

Figure 4 shows that the zoning approach always guarantees positive distance gaps between UAVs, which reduces the relevant collision avoidance efforts. On the other hand, the benchmark service strategies incur very close and even crossing UAV flights (i.e., zero distance gap). Recall that under the zoning approach, a flight path is generated within a zone, and thus it does not overlap with other paths in other zones. As collision avoidance is complex and might be infeasible when the distance between UAVs is tight, the safety net provided by the zoning approach is promising. In other words, the zoning approach can guarantee the safety of UAVs and the reliability of the UAV-based system, especially when large-scale UAV operations are considered.
