## 2 Methodology
### Comprehensive Evaluation  
Conventional CE typically involves the handling of indicators across various fields, each containing varying amounts of information. Among these fields, weight assignment plays a significant role. However, since each expert always holds individual views on the importance of design performance indicators in various aspects, reaching a unified and accepted conclusion is difficult, resulting in subjective evaluation that can lead to excessive randomness. In such a case, an objective approach can be more appropriate. Table 1 presents a comparison of weight assignment methods frequently used in CE. This comparison provides insight into the mechanics of these methods and reflects the adaptation scene. Road transportation has been commonly recognized as a complex organic system consisting of interdependent factors across various aspects, whereas the CRITIC method holds features that match handling the volatility and correlation of traffic data. The two crucial parameters, volatility (contrast intensity) and conflict (correlation), are positively associated with the assigned weight. The standard deviation and correlation coefficient are used to express the two parameters, respectively. Higher contrast intensity stands for higher volatility, whereas the correlation coefficient represents the opposite of the conflict. The specific calculation is shown from Equation (4) to Equation (13). 1. Indicator results matrixin \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Data Size** & **Data Volatility** & **Data Correlation** & **Remarks** \\ \hline Entropy & N/A & N/A & N/A & entropy, information amount \\ CRITIC & N/A & \(\surd\) & \(\surd\) & \\ Factor Analysis Method & N/A & N/A & \(\surd\) & information concentration \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of three typical weight assignment methods in CE. \[A_{k}= \begin{bmatrix}V_{1,k},T_{1,k},F_{1,k},C_{1,k},D_{1,k}\\ V_{2,k},T_{2,k},F_{2,k},C_{2,k},D_{2,k}\\ \vdots\vdots\vdots\vdots\vdots\\ V_{i,k},T_{i,k},F_{i,k},C_{i,k},D_{3,k}\end{bmatrix}  \tag{4}\]  
As shown in Equation (4), Matrix \(A_{k}\) for original data in the scene \(k\) contains the five indicators for each design, where \(V_{i,k}\), \(T_{i,k}\), \(F_{i,k}\), \(C_{ijk}\), and \(D_{i,k}\) denote capacity, travel time, fuel consumption, conflicts, and delay of the \(i\)th design. 2. Data normalization. \[X=\left[x_{1},x_{2},x_{3},x_{4},x_{5}\right] \tag{5}\]  
\[x_{j}=\left[x_{1j},x_{2j},\cdots,x_{ij}\right]^{T} \tag{6}\]  
Data normalization aims to eliminate the effects of dimensionality and measure data with a uniform standard, before which each indicator for different designs will be transferred to \(x_{j}\), as shown in Equations (5) and (6). \[x^{\prime}_{ij}=\frac{x_{ij}-\max\{x_{1j},x_{2j},\cdots,x_{ij}\}}{\max\{x_{1j},x_{2j},\cdots,x_{ij}\}-\min\{x_{1j},x_{2j},\cdots,x_{ij}\}} \tag{7}\]  
Equation (7) shows forward normalization for \(x_{ij}\), which denotes capacity and is expected to perform at a higher value. Conversely, the other four that are expected to lower take the inverse normalization process in Equation (8). \[x^{\prime}_{ij}=\frac{\max\{x_{1j},x_{2j},\cdots,x_{ij}\}-x_{ij}}{\max\{x_{1j},x_{2j},\cdots,x_{ij}\}-\min\{x_{1j},x_{2j},\cdots,x_{ij}\}} \tag{8}\]  
3. Calculating information volume.