## 5 Conclusions  
In this paper, we discussed the concept of VANET and the existing challenges in this area, we provided a detailed taxonomy for ridesharing, carsharing, VEC, and the traffic improvement application in vehicular networks, where various challenges were mentioned and a detailed solution was discussed. Furthermore, in order to demonstrate the effectiveness of using agile optimization algorithms in the concept of VANET. We combined \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**State**} & \multicolumn{4}{c}{**Random Scenario**} \\ \cline{2-4}  & **Avg\_Distance (1)** & **Avg\_Capacity (2)** & **Avg\_Facility\_Number (3)** \\ \hline Very\_fluid & 2186.30 & 56 & 10 \\ Fluid & 1205.78 & 111 & 19 \\ Dense & 889.56 & 164 & 28 \\ Very\_dense & 658.34 & 220 & 36 \\ \hline \hline \multicolumn{4}{c}{**Constructive Scenario**} \\ \cline{2-4}  & **Avg\_Distance (4)** & **Avg\_Capacity (5)** & **Avg\_facility\_Number(6)** \\ \hline Very\_fluid & 3127.12 & 55 & 7 \\ Fluid & 1521.98 & 108 & 13 \\ Dense & 1059.61 & 163 & 24 \\ Very\_dense & 686.28 & 216 & 34 \\ \hline \hline \multicolumn{4}{c}{**Gap**} \\ \cline{2-4}  & **Gap (1)–(4)** & **Gap (2)–(5)** & **Gap (3)–(6)** \\ \hline Very\_fluid & \(-43.03\%\) & 1.79\% & 30.00\% \\ Fluid & \(-26.22\%\) & 2.70\% & 31.58\% \\ Dense & \(-19.12\%\) & 0.61\% & 14.29\% \\ Very\_dense & \(-4.24\%\) & 1.82\% & 5.56\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparative results between constructive scenario and random scenario. Figure 12: Performance of different scenarios. state-of-the-art algorithms with vehicular networks, using real data from Barcelona's open data repository to solve the CDP using constructive heuristics because the constructive approach is built by adding promising elements one by one until the required capacity is reached. Different values are considered for the threshold capacity based on the city's traffic level, which was determined as a proportion of the network's total potential capacity. In order to demonstrate the efficacy of the constructed heuristic in a real-world scenario, we evaluated its performance and compared it to a random scenario. As a result, the constructive heuristic outperforms the random scenario by maximizing distances between facilities while satisfying the required capacity with the fewest facilities. Additionally, increasing the traffic volume from very fluid to very dense resulted in a general downward trend in solution quality and necessitated a high capacity percentage in order to open more facilities. Consequently, using constructed heuristic would improve QoS in VANET by making better use of available resources. In the future, a comparison of this work to the state of the art will be considered. We can also extend it by using predictive models, i.e., machine learning models, to predict facility capacity rather than predetermined capacity and by providing a dynamic model where the threshold changes based on the dynamic situation of the environment. Furthermore, we would like to use a simulator to simulate a realistic scenario of transportation systems with V2X communication and an intelligent roadside unit in order to evaluate the model's ability to respond adequately to edge node mobility. Conceptualization, F.X. and A.A.J. ; methodology, M.P., J.P. and A.A.J. ; validation, C.S. and F.X. ; investigation, T.F. and M.P. ; writing--original draft preparation, T.F. and M.P. ; writing--review and editing, M.P. and J.P.; supervision, F.X., J.P. and C.S. All authors have read and agreed to the published version of the manuscript. This work has been partially funded by the Spanish Ministry of Science (PID2019-111100 RB-C21 /AEI/ 10.13039/501100011033), as well as by the Barcelona City Council and Fundacio \"la Caixa\" under the framework of the Barcelona Science Plan 2020-2023 (grant 21S09355-001). Not applicable. The data used in this study are obtained from Open Data Barcelona. The authors declare no conflict of interest.