# Data-Driven Deployment of Cargo Drones: A U.S. Case Study Identifying Key Markets and Routes
## 4 Results and Discussions
### Distance Band Truncation  
Table 7 displays the truckload equivalents transported among the top eight MSAs in four distance bands for each of the four commodity categories selected. Movements across all distances (not shown in the table) required 8,604,968 truckload equivalents in 2017, which accounted for 30.4% of the truckloads in the CONUS. The number of truckload equivalents will raise by 35% by 2040 even though the proportion of truckloads will remain similar [15].  
Figure 7 shows the distribution of truckload equivalents across all distance bands. In summary, truck transport within 100 miles accounted for 39.3% of the truckload equivalents for the four selected commodity categories. The accumulated proportion (% Acc) moved was 80.5% within a 400-mile distance band. Hence, 400 miles represented a point of diminishing returns for transporting those commodities by air. Current projections based on the anticipated improvements of battery technology suggest that cargo EAAs will exceed those ranges well before 2050 [26].  
In a comparison with the existing literature, the results of the data-driven approach to optimizing cargo shipping with EAA provide a unique contribution to the field. While previous studies have explored the potential of EAA in various contexts, as summarized earlier in Table 1, none have specifically focused on using a data mining and GIS workflow to optimize cargo shipping with EAA. The findings of this study align with a key observation by the USDOT's national freight strategic plan, which anticipates a faster growth rate in overall transport demand for low-weight, high-value commodities than for high-weight,  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**MSA** & **Pharmaceuticals** & **Machinery** & **Electronics** & **Mixed Goods** & **Total KTons** \\ \hline Los Angeles CA, USA & 2725.7 & 14,866.3 & 15,523.6 & 32,170.3 & 65,286.0 \\ San Francisco CA, USA & 4441.2 & 3331.6 & 3707.0 & 19,142.1 & 30,622.0 \\ Tampa FL, USA & 12,360.8 & 759.9 & 1040.1 & 7532.0 & 21,692.8 \\ Atlanta GA, USA & 411.1 & 5975.9 & 3195.6 & 18,506.3 & 28,088.8 \\ Chicago IL, USA & 3026.0 & 7069.2 & 4867.2 & 23,411.6 & 38,374.0 \\ New York NY, USA & 8794.0 & 3132.8 & 2646.3 & 26,132.2 & 40,705.3 \\ Dallas–Fort Worth TX, USA & 3061.4 & 5071.5 & 5716.2 & 30,506.5 & 44,355.6 \\ Houston TX, USA & 8724.7 & 9928.9 & 4367.9 & 16,874.2 & 39,895.7 \\ \hline Total & 43,545.0 & 50,136.1 & 41,064.0 & 174,275.2 & 309,020.2 \\ CONUS & 302,783.0 & 237,632.8 & 146,415.5 & 848,330.2 & 1,535,162 \\ Top MSA \% & 14.4\% & 21.1\% & 28.0\% & 20.5\% & 20.1\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Commodity weight moved in the top MSAs.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Miles Band** & **Mixed Goods** & **Electronics** & **Machinery** & **Pharma** & **Total** & **\%** & **\% Acc** \\ \hline
100 & 2,583,396 & 299,612 & 419,312 & 80,519 & 3,382,840 & 39.3\% & 39.3\% \\
200 & 1,357,662 & 257,587 & 288,219 & 44,434 & 1,947,902 & 22.6\% & 61.9\% \\
300 & 591,588 & 154,589 & 236,029 & 46,440 & 1,028,646 & 12.0\% & 73.9\% \\
400 & 326,591 & 122,995 & 98,965 & 14,829 & 563,380 & 6.5\% & 80.5\% \\ \hline Totals & 4,859,238 & 834,782 & 1,042,526 & 186,222 & 6,922,768 & 80.5\% & \\ \hline \hline \end{tabular}
\end{table}
Table 7: Truckload equivalent flows between the top MSAs.  
low-value commodities. The finding that a distance band of 400 miles among eight regional locations accounts for more than 80% of the transported weight provides a practical guide for planners and investors in identifying prime target markets for EAAs, a gap that previous studies did not address. While these findings are consistent with the broader literature on the potential of EAA for cargo shipping, this contribution of a data-driven approach to optimize cargo shipping with EAA is unique.