## 5 Conclusions  
Our proposed approach provides a novel and effective solution to optimize the routing of electric delivery vehicles in urban areas, considering both topographic and traffic information to reduce electric consumption. Our algorithm demonstrated a significant reduction in energy consumption while only requiring a slight increase in distance compared to the traditional distance-based approach. The efficiency and speed of our algorithm make it suitable for real-time routing applications, enabling fast and reliable solutions for large-scale STSP instances.  
Future investigation could combine the proposed methodology with a more efficient routing algorithm in a negatively weighted graph. Furthermore, considering the integration of the clustered generalized Traveling Salesman Problem methodology into our proposed algorithm holds potential for exploration.  
Furthermore, we have made the weighted city graphs, which incorporate elevation data, available to the public to facilitate the analysis of different electric routing problems, taking into account traffic data and height variations along the route. We hope these data and methods will encourage further research in the field and facilitate the development of more efficient and sustainable routing strategies for electric delivery vehicles.  
Overall, our proposed approach permits the development of more efficient routing strategies for EVs in the delivery industry. It can potentially improve the operational efficiency of delivery companies while reducing their environmental impact, thus contributing to a more sustainable future.  
Conceptualization, Y.A., P.D.-M., B.I., A.R., A.M. ([PERSON]), A.M. ([PERSON]) and J.A.C.; methodology, Y.A.; software, Y.A. and P.D.-M.; validation, Y.A. and P.D.-M.; formal analysis, Y.A., P.D.-M., B.I. and J.A.C.; investigation, Y.A., P.D.-M., B.I., A.R., A.M. ([PERSON]), A.M. ([PERSON]) and J.A.C.; data curation, Y.A. and P.D.-M.; writing--original draft preparation, Y.A., P.D.-M., B.I. and J.A.C.; writing--review and editing, Y.A., P.D.-M., B.I. and J.A.C.; visualization, Y.A. and P.D.-M.; supervision, J.A.C.; project administration, Y.A.; funding acquisition, J.A.C. All authors have read and agreed to the published version of the manuscript.  
J.A.C is supported by grant PID2022-138860 NB-I00 funded by MCIN/ AEI/ 10.13039/50110 0011033 and, as appropriate, by \"ERDF A way of making Europe\", by the \"European Union\" or by the \"European Union NextGenerationEU/PRTR\".  
All the data used to build the city graphs and the models alongside the Python code is available in the Supplementary Materials [24]. The elevation data can be obtained from the [[https://centrodedescargas.cnig.es/CentroDescargas/index.jsp](https://centrodedescargas.cnig.es/CentroDescargas/index.jsp)]([https://centrodedescargas.cnig.es/CentroDescargas/index.jsp](https://centrodedescargas.cnig.es/CentroDescargas/index.jsp)) (accessed on 18 July 2023), CNIG download center and the traffic data from [[https://bit.ly/3](https://bit.ly/3) se5j5G]([https://bit.ly/3](https://bit.ly/3) se5j5G) (accessed on 18 July 2023), Madrid's open data website.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline  & \multicolumn{2}{c}{**Madrid5**} & \multicolumn{2}{c}{**Madrid10**} & \multicolumn{2}{c}{**Madrid15**} \\ \cline{2-7}  & **Energy** & **Distance** & **Energy** & **Distance** & **Energy** & **Distance** \\ \hline Travel distance (km) & 44.19 & 41.2 & 69.73 & 64.22 & 89.23 & 83.51 \\ Travel time (min) & 63.99 & 50.15 & 99.57 & 78.38 & 126.24 & 101.53 \\ Average speed (km/h) & 41.43 & 49.29 & 42.02 & 49.16 & 42.38 & 49.35 \\ Energy consumption (kWh) & 6.72 & 8.24 & 10.71 & 12.92 & 13.83 & 16.67 \\ Energy efficiency (km/kWh) & 6.58 & 5 & 6.5 & 4.97 & 6.45 & 5 \\ \hline \end{tabular}
\end{table}
Table 2: Comparing energy consumption and distance-based STSP solving approaches.