# Authenticity, and Approval Framework for Bus Transportation Based on Blockchain 2.0 Technology
## 6 Methodology
Tabu search is a computational optimization algorithm designed to effectively navigate solution spaces, particularly in scenarios characterized by numerous potential solutions and intricate search landscapes. The genetic algorithm (GA) is an optimization technique inspired by natural selection and genetics [66]. It is commonly employed to obtain approximate solutions for optimization and search issues. Genetic algorithms demonstrate significant efficacy in addressing problems with extensive solution spaces and various feasible solutions. Table 10 presents the processing time performance. Figure 11 illustrates that as we increased the number of users by a constant number of application requests per user the default approach resulted in application access delays, due to the number of pending transactions in processing nodes. Conversely, the processing time decreased with the stochastic algorithm GA approach, leading to fewer pending processing transactions at the nodes. Furthermore, this processing time decreased even more with the stochastic algorithm, the Tabu search approach. \begin{table}
\begin{tabular}{|c|c c c|} \hline
**Index** & **Number of Users** & **Aurora Testnet** & **Ethereum Mainnet** \\ \hline
1 & 10 & 60\% & 70\% \\ \hline
2 & 20 & 55\% & 75\% \\ \hline
3 & 100 & 45\% & 80\% \\ \hline
4 & 500 & 40\% & 90\% \\ \hline \end{tabular}
\end{table} 
Table 7: Figure 9(b). Comparison between Aurora testnet and Ethereum mainnet regarding security. \begin{table}
\begin{tabular}{|c|c c c|} \hline
**Index** & **Number of Users** & **Aurora Testnet** & **Ethereum Mainnet** \\ \hline
1 & 10 & 90\% & 100\% \\ \hline
2 & 20 & 85\% & 98\% \\ \hline
3 & 100 & 80\% & 95\% \\ \hline
4 & 500 & 75\% & 90\% \\ \hline \end{tabular}
\end{table} 
Table 8: Figure 9(c). Comparison between Aurora testnet and Ethereum mainnet regarding performance. \begin{table}
\begin{tabular}{|c|c c c|} \hline
**Index** & **Number of Users** & **Aurora Testnet** & **Ethereum Mainnet** \\ \hline
1 & 10 & 700 & 900 \\ \hline
2 & 20 & 1500 & 2500 \\ \hline
3 & 100 & 7500 & 9000 \\ \hline
4 & 500 & 20,000 & 35,000 \\ \hline \end{tabular}
\end{table} 
Table 9: Figure 9(d). Comparison between Aurora testnet and Ethereum mainnet regarding gas cost. ### DEMATEL Analysis

A DEMATEL analysis was further performed for the BC-BAS. The Geneva Research Centre of the Battelle Memorial Institute (GRCBMI) invented the DEMATEL technique, in order to illustrate the structure of complex causal interactions using matrices or digraphs [68]. It is particularly helpful in examining the cause-and-effect interactions between system components, as a type of structural modeling methodology. The DEMATEL is used to investigate and resolve complex, interconnected situations because it proves the interdependence of elements and helps create a map that reflects the relative relationships between them. In pursuit of this analysis, we identified nine critical factors: transaction cost \(F_{1}\), execution time \(F_{2}\), memory consumption \(F_{3}\), processing time \(F_{4}\), security \(F_{5}\), portability \(F_{6}\), scalability \(F_{7}\), transparency \(F_{8}\), and reliability \(F_{9}\). The segments of the DEMATEL analysis are as follows, as shown in Table 11:

* We took the expert's opinion on the BC-BAS in the form of a survey, based on the factors identified in Table 11, and we formed a direct influence matrix, \(X\), shown in Table 12, accounting for the expert score criteria mentioned. The triangular fuzzy number (TFN) method was used to transform human linguistics into a commutable form. The transformation table had the following information: N (no influence) had TFN (0, 0, 0.2), very low influence (VL) had TFN (0, 0.2, 0.4), low influence (L) with TFN (0.2, 0.4, 0.6), high influence with TFN (0.4, 0.6, 0.8), and very high influence (VH) with TFN (0.8, 1, 1) [2]. * After obtaining the direct influence matrix, \(X\), normalization was performed according to Equation (2), where \(n\) is the total number of elements in \(X\).