# Coordination in Emergency Response System Design:
## 8 Conclusions  
This paper has been motivated by the need to design emergency response networks that comprise teams from multiple agencies so that their arrival at the scene of emergency is well-coordinated. Focusing on the problem with two response resources, we can define the poor, adequate, and preferred responses in terms of the target response and wait times. This enables us to expand the maximal covering problem formulation to incorporate two resources, where the objective is to maximize the number of possible emergency sites with the preferred response. The conceptual framework is generalizable to more than two resources, although the number of response types would be combinatorial and, hence, the arising mathematical models would be cumbersome. From the perspective of policymakers and emergency planners, our findings suggest that significant improvements could be achieved by deploying a systems view in planning \begin{table}
\begin{tabular}{l c c c c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Independent Design**} & \multicolumn{6}{c}{**Coordinated Design**} \\ \hline
**[NA,NF]** & **[62,25] +** & **[62,30] +** & **[62,35] +** & **[62,40] +** & **[62,45] +** & **[67,25]** & **[72,30]** & **[77,35]** & **[82,40]** & **[87,45]** \\  & **[67,20]** & **[72,20]** & **[77,20]** & **[82,20]** & **[87,20]** & **[87,20]** & **[87,20]** & **[77,35]** & **[82,40]** & **[87,45]** \\ \hline Max \# of New & & & & & & & & & & & & & \\ \hline Ambance St. & 0 & 5 & 0 & 10 & 0 & 15 & 0 & 20 & 0 & 25 & 5 & 10 & 15 & 20 & 25 \\ Fire St. & 5 & 0 & 10 & 0 & 15 & 0 & 20 & 0 & 25 & 0 & 5 & 10 & 15 & 20 & 25 \\ \hline \# of Nodes & & & & & & & & & & & & & & \\ \hline Poor \(\rightarrow\) Pref. & 133 & 153 & 165 & 169 & 169 & 133 & 178 & 203 & 223 & 234 \\ Adeq. \(\rightarrow\) Pref. & 31 & 36 & 40 & 39 & 40 & 33 & 34 & 36 & 39 & 40 \\ \hline Average WT (min) & 2.20 & 2.01 & 1.77 & 1.80 & 1.80 & 2.06 & 1.98 & 1.73 & 1.50 & 1.43 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparing the solutions with independent and coordinated design. emergency response networks. Typically, these systems are expanded gradually, i.e., the decisions to add one (or more) ambulance or fire station(s) are made sequentially, as budgets become available. This paper clearly highlights the need to have a long-term perspective for expanding the existing networks, rather than adopting a myopic approach by focusing on the best sites for only the new stations to be established in the short term. Our work also points out the need for streamlining the network development process for the specialty teams that need to collaborate, although their organizational structure can be separate, e.g., ambulance services and fire services. A collaborative development of such separately managed systems would certainly pay off in terms of the joint response time to the incidents. This paper is at the strategic level, focusing on the structure of emergency response systems to maximize the coordination among multiple specialty teams. At the tactical level, these systems have benefited significantly from artificial intelligence (AI)-based enhancements. Ref. [33] is a recent systematic review of the literature on AI-based emergency response systems. Among several enhancements, the authors highlighted that AI has significantly improved emergency vehicle routing and congestion management by enabling real-time traffic analysis, dynamic route optimization, and predictive analytics. The AI-based enhancements in real-time decision making and communication for emergency response are also discussed in [34]. Our work is a first step in the development of an analytical framework for the coordination of the multiple resources involved in emergency response. It has several limitations, however, that need to be resolved in future work to improve its performance in practical settings. For example, we do not consider traffic congestion and the resulting uncertainties in our modeling framework.