# An Optimization Route Selection Method of Urban Oversize Cargo Transportation
## 4 Discussion  
As a comparison, this article compares the calculation results using only the TOPSIS method and the entropy weight-TOPSIS method with the calculation results used in our research. The calculation results of the three methods are shown in Table 8:  
It can be seen that when only the TOPSIS method is used, the ranking of four routes is \(C_{2}>C_{3}>C_{1}>C_{4}\). When the entropy weight-TOPSIS method is used, the ranking of four routes is \(C_{2}>C_{3}>C_{1}=C_{4}\). In order to verify the feasibility of the proposed method, we selected the transportation of oversize cargo across Suzhou in the last 5 years for comparison as shown in Table 9.  
It can be seen that in the actual route selections of oversize cargo transportation in the past five years, 22 transportations have occurred. Among them, there are three selections for route 1, nine selections for route 2, five selections for route 3, and four selections for route 4. The percentages are 0.136, 0.409, 0.227, and 0.182. The Pearson correlation coefficients between the three methods and the actual transportation route selections were checked; the Pearson correlation coefficients are shown in Table 10:  
According to the analysis, it can be concluded that the matching degree between the optimization weight-TOPSIS method proposed in this research and the actual transportation route selections is significantly higher than the TOPSIS method, and slightly higher than the entropy weight-TOPSIS method. In the actual urban transportation of oversize cargo, in addition to objective conditions, the subjective route choice is also very important. Therefore, subjective judgment should be appropriately introduced in the design of the route selection algorithm to make the calculation results more realistic.  
Regarding the calculation of the TOPSIS, the authors believe that the calculation method used in this paper is effective for the situation discussed in this case. In some special cases, such as when the solution plane is difficult to reach, the positive ideal solution plane and the negative ideal solution plane can be adjusted to meet the requirements of variables and practical applications.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Pearson Correlation Coefficient** \\ \hline TOPSIS & 0.938 \\ Entropy weight–TOPSIS & 0.977 \\ Optimization weight–TOPSIS & 0.995 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Pearson correlation coefficient.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Route Section** & \begin{tabular}{c} **Relative** \\ **Proximity** \\ \end{tabular} & **TOPSIS** & \
\begin{tabular}{c} **Entropy** \\ **Weight-TOPSIS** \\ \end{tabular} &
\begin{tabular}{c} **Optimization** \\ **Weight-TOPSIS** \\ \end{tabular} \\ \hline R1 & \(C_{1}\) & 0.27 & 0.27 & 0.28 \\ R2 & \(C_{2}\) & 0.69 & 0.73 & 0.77 \\ R3 & \(C_{3}\) & 0.5 & 0.46 & 0.4 \\ R4 & \(C_{4}\) & 0.25 & 0.27 & 0.32 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Relative proximity of three methods.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Year** & **R1** & **R2** & **R3** & **R4** \\ \hline
2016 & 0 & 2 & 1 & 2 \\
2017 & 0 & 1 & 0 & 0 \\
2018 & 1 & 1 & 2 & 1 \\
2019 & 2 & 2 & 1 & 1 \\
2020 & 0 & 3 & 1 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Actual route selections of oversize cargo transportation.