# Emergency Planning for UAV-Controlled Crowd Evacuations
## 5 Conclusions and Future Work  
This paper suggests a simulation model to show the potential of utilizing UAVs in crowd management during emergency evacuations, with two UAV guidance approaches: partial guidance and full guidance. We examined the effect of two different placements of exit doors (opposite and adjacent) and three UAV positions (middle, near exit, and random) for the partial guidance model, and two UAV positions (middle, and random) for the two exit positions for the full guidance model, testing the efficacy of the various scenarios in the evacuation of an indoor hall. The simulations were performed with low and high crowd densities, which affected the pedestrians' walking speeds accordingly.  
The performance of the models was measured according to the average evacuation time and mortality rate. The results showed the superiority of the partial guidance model with opposite exit doors, regardless of the position of the UAVs: this scenario significantly decreased the mortality rate and produced a remarkably low average evacuation time when compared to other approaches.  
The results presented herein open a path toward research on various possibilities to improve emergency planning for UAV-controlled crowd evacuations. Future studies might examine different shapes and sizes of indoor hall, with different numbers of obstacles, exit doors, and UAVs. In addition, the simulation could be extended by considering  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**UAV Leader Model** & **Recommended UAV Position** & **Recommended Exit Placement** \\ \hline Partial & Middle, random, or near exit & Opposite \\ \hline Full & Middle & Opposite \\ \hline \hline \end{tabular}
\end{table}
Table 4: Summary of results.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref.** & **Focus** & **Simulation** & **Exit (s)** & **Evacuation Time** & **Mortality Rate** & **Scaling Up (\# of Pedestrians)** \\ \hline
[16] & Providing instructions & Unity3D game engine & Multiple at fixed locations & Improved & NA & Up to 400 \\ \hline
[18] & UAV scheduling & NA & Single at a fixed location & NA & NA & NA \\ \hline
[21] & Multi-UAV cooperation & DASH & Single at a fixed location & NA & NA & NA \\ \hline Ours & Crowd guidance & NetLogo & Multiple at different locations & Improved & Improved & Up to 7200 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Qualitative comparison of our proposed methodology with other existing research.  
various characteristics that affect pedestrians' walking speed, such as age, gender, and medical condition.  
Methodology, A.A. ([PERSON]), D.A., A.A. ([PERSON]) and R.A.; software, A.A. ([PERSON]), D.A., A.A. ([PERSON]) and R.A.; validation, A.A. ([PERSON]), H.K.; formal analysis, A.A. ([PERSON]), H.K., N.A.N. and A.B.M.A.A.I.; investigation, A.A. ([PERSON]), N.A.N. and A.B.M.A.A.I.; writing--original draft preparation, D.A., A.A. ([PERSON]) and R.A.; writing--review and editing, A.A. ([PERSON]), H.K., N.A.N. and A.B.M.A.A.I.; visualization, A.A. ([PERSON]); supervision, H.K. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia through the project number (DRI-KSU-762).  
Not applicable.  
The authors extend their appreciation to the Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia for funding this research work.  
The authors declare no conflict of interest associated with this paper.