# Emergency Planning for UAV-Controlled Crowd Evacuations
## 3 Methodology  
In this study, we used an agent-based modeling and simulation platform, NetLogo 6.1.1 [22], to model the environment and test a wide range of evacuation scenarios. The environment represented a 60 m \(\times\) 60 m (3600 m\({}^{2}\)) indoor hall, as shown in Figure 1, with four exits that are 3 m wide each(white rectangles) and six randomly placed obstacles (marked in green), each representing a long pillar of1 m radius. We examined the effect of two placements of exit doors, opposite or adjacent, as shown in Figure 2, where the width of the exit doors was 3m. We considered different numbers [450, 900, 1800, 3600, and 7200] ofarbitrarily located standing pedestrians, and their walking speeds were varied depending on the crowd density (CD) [23] as shown in Table 2.  
Figure 1: Model Elements. Figure 2: Arrangements for the placement of four exits. For guided evacuation, we considered four battery-powered mini-multi-rotor UAVs capable of a speed of 3.5 m/s. Each UAV was equipped with a light to guide the pedestrians in case of darkness. One depot was placed next to each exit door and four other depots were distributed in the hall based on the scenario being tested. For example, if a scenario was set up to test the central position of the UAVs, then the depots were also located in the center. In this case, when the evacuation starts, a UAV flies from a central depot to a depot near the exit. We experimented with two UAV leader models:  
Partial guidance: In this approach, a pedestrian immediately goes to an exit door if it is closer to them than a UAV. Otherwise, the pedestrian follows the nearest UAV, which guides them to the nearest exit. This approach can be followed when the UAVs are positioned either randomly, near an exit, or in the middle of the hall. Full guidance: In this approach, each pedestrian follows the nearest UAV, which guides them to the nearest exit. This approach can be followed when the UAV is positioned randomly or in the middle of the hall. In both of the above leader models, a pedestrian can take a decision simply by observing the UAV and without following any iterative or complex process. Thus, the time complexity of decision-making by a pedestrian in our methodology is O (1). We also investigated the evacuation time when following our methodology. The results on evacuation time are presented in Section 4. To better investigate the outcomes of our methodology, we varied the initial location of the UAVs to explore different possible positions:  
* Middle: All UAVs are located at the center of the hall. Once the evacuation starts, a UAV flies to the nearest exit where all (in case of full guidance) or some (in case of partial) of its nearby pedestrians follow it. * Random: Each UAV is initially placed in an arbitrary position in each area. Once the evacuation starts, a UAV flies to a different exit where all (in case of full guidance) or some (in case of partial) of its nearby pedestrians follow it. * Near exits: Each UAV is assigned to an exit position. Once the evacuation starts, a UAV directs its nearby pedestrians to that exit. The different settings of all considered variables are summarized in Table 3.  
In [24] the average time of the evacuation process was examined, and it was found that 15 min is a reasonable time between the instant that pedestrians are made aware of an emergency and their arrival at safe location. Therefore, in our experiments, we limited the evacuation time to15 min in both partial and full guidance models. Two performance metrics were used to test the effectiveness of the two UAV guidance approaches:  
* Average evacuation time (AET): the average time each evacuee takesto reach the designated exit. * Mortality: the number of deaths that occur in the evacuation process. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Number of Pedestrians** & **Crowd Density (Person/m2)** & **Walking Speed (m/s)** \\ \hline
450, 900, 1800 & \(\leq\)0.5 & 1.2 \\
3600 & 1 & 1.03 \\
7200 & 2 & 0.66 \\ \hline \hline \end{tabular}
\end{table}