## 2 Materials and Methods
Moreover, we included a comprehensive Supplementary Materials spanning 56 pages, which contains in-depth explanations, algorithmic flowcharts, pseudocode, parameter definitions, and the underlying modeling logic used in our proposed framework. Readers can refer to the Supplementary Materials for complete details supporting the reproducibility and transparency of our methodology. This study is designed based on the OA (\(L_{9}(3^{3})\)) method. For the similarity between control factors and their level, in this work, two OA tables are used for QOS-AODV and GPSR optimization and presented in Tables 3 and 4 sequentially. For each optimization problem, a set of 9 experiments was conducted, and each experiment was repeated for different random seed generations. The random seed generation represents the noise factors in Taguchi design, and hence, a minimum of 5 repetitions per experiment were applied. In this study, the L9(\(3^{3}\)) Orthogonal Array was chosen to balance experimental efficiency and statistical reliability. While a full factorial design would require 27 runs, the L9 design reduces this to 9, significantly lowering computational cost while still capturing the main effects of three factors at three levels. The L9 array ensures orthogonality, enabling unbiased comparison of factor levels, and supports robust optimization through the use of signal-to-noise (S/N) ratios. This approach is widely accepted in engineering and network performance studies for its ability to efficiently screen variables and assess performance trends. Given the exploratory nature of this work, the L9 design is appropriate for identifying key performance influences without the overhead of exhaustive experimentation. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**No** & **MaxJitter** & **ActiveRouteTimeOut** & **HelloInterval** \\ \hline
1 & 0.1 s & 20 s & 1 s \\
2 & 0.1 s & 30 s & 10 s \\
3 & 0.1 s & 40 s & 20 s \\
4 & 0.5 s & 20 s & 10 s \\
5 & 0.5 s & 30 s & 20 s \\
6 & 0.5 s & 40 s & 1 s \\
7 & 1.0 s & 20 s & 20 s \\
8 & 1.0 s & 30 s & 1 s \\
9 & 1.0 s & 40 s & 10 s \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Orthogonal Array for QOS-AODV Experiment Design: The table presents the structured experimental design matrix based on an Orthogonal Array. The matrix was used to systematically evaluate the effects of multiple QoS-AODV inner parameters on network performance with a minimal number of simulation runs. The experimental design of the TOM (Taguchi Orthogonal Method) is structured to evaluate specific output targets, namely Throughput, delay, and packet delivery ratio (PDR), which were analyzed during the TOM analysis phase. The experimental results were collected from multiple trials to facilitate the calculation of objective functions. In this study, the OMNET++ simulator and INET3.5 protocol stack were employed to conduct the experiments with the VEINS for mapping the city to a simulation scenario as presented in our previous publications [23; 26]. The TOM method outlines two primary analysis goals. The first is to assess the impact of control factors on the output, which is achieved through delta analysis of the loss function. The second goal is to determine the optimal configuration of control factors for a given output. This is carried out by identifying the highest average signal-to-noise ratio (SNR) corresponding to a specific level of the control factor. Thus, in analyzing the effect of control factors on delay in QOS-AODV protocols under a city scenario, the experimental results are presented in Table 5. Each experiment was repeated for five trials, with the average delay values recorded in columns T1 through T5. The initial step is to apply the \"smaller-the-better\" loss function (LF) for each experiment. Subsequently, the average SNR of the loss function is calculated for each level of a control factor. To evaluate the protocol's performance under increasing node density and traffic volumes to assess its robustness in high-density VANET scenarios, we employed the BAHG simulation.