## 2 Methods
### Association Analysis of Evaluation Indicators and Influential Factors  
#### 2.3.1 Sensitivity Analysis of Influential Factors and Value of Influence  
Sensitivity analysis is a method to measure the degree of influence of parameters on a system and can be divided into single-factor and multi-factor sensitivity analysis. Single-factor analysis refers to the technique in which the value of one uncertain factor is changed within a specific range at a given time while other factors are kept fixed, and then changes in the corresponding model are examined [25,26]. Single-factor sensitivity analysis, on the one hand, is an effective numerical processing method that can be used to obtain a data sequence of influential factors and the value of influence. On the other hand, by adjusting each influencing factor one by one, observing its impact on the value of influence can help determine which factors have a greater impact on the value of influence. Therefore, based on the theory of this method, the influencing factors of the operational effect of the green wave are taken as uncertain factors. The value of influence reflects the behavior characteristics of the system. A single-factor sensitivity analysis is proposed to calculate the values of influencing factors and value of influence. Specific analysis methods are as follows:  
We used the value of influence as the index of sensitivity analysis and used influencing factors as the uncertain factors. The actual values of the design speed, signal timing, pedestrian crossing, and heavy vehicles were used as benchmark values to set the variation range in their values, as shown in Equations (9)-(12):  
\[V_{j}\in[V_{0}-\Delta V,V_{0}+\Delta V], \tag{9}\]  
\[C_{j}\in[C_{0}-\Delta C,C_{0}+\Delta C], \tag{10}\]  
\[L_{j}\in[L_{0}-\Delta L,L_{0}+\Delta L], \tag{11}\]  
\[H_{j}\in[H_{0}-\Delta H,H_{0}+\Delta H], \tag{12}\]  
among these, \(V_{0}\), \(C_{0}\), \(L_{0}\), and \(H_{0}\), respectively, represent the actual values of \(V\), \(C\), \(L\), and \(H\); \(V_{j}\), \(C_{j}\), \(L_{j}\), and \(H_{j}\), respectively, represent the \(j\)th value of \(V\), \(C\), \(L\), and \(H\); and \(j\) values generally range from 1 to 5; that is, \(j\) = 1, 2, 3, 4, 5 [25,26], and \(\Delta V\), \(\Delta C\), \(\Delta L\), and \(\Delta H\), respectively, represent the variations of \(V\), \(C\), \(L\), and \(H\).  
Under the premise that the benchmark values of other uncertain factors are taken, 5 different values of an uncertain factor are taken within its variation range, and the corresponding values of influence are calculated, respectively. For example, the values of \(C\), \(L\), and \(H\) are \(C_{0}\), \(L_{0}\), and \(H_{0}\), respectively, and the values of \(V\) are \(V_{j}\) (\(j\) = 1, 2, 3, 4, 5). \(V\) is the first influencing factor, so the value of \(i\) is 1. The Vissim simulation software is used to output the values of \(S_{1j}\), \(T_{1j}\) and \(D_{1j}\) corresponding to \(S\), \(T\), and \(D\), and then \(E_{ij}\) of \(E\) is calculated by Equations (1)-(4). After all the numerical \(E_{ij}\) of \(E\) is calculated, the effects of \(V\), \(C\), \(L\), and \(H\) on \(E\) are further analyzed by simple regression analysis. Origin software can be used to draw the diagram of the linear fitting of \(V\), \(C\), \(L\), \(H\) and \(E\), respectively. If the correlation coefficients \(R\) of \(V\), \(C\), \(L\), \(H\) and \(E\) pass the test, \(V\), \(C\), \(L\), \(H\) and \(E\) are obviously shown to be correlated; otherwise, there is no correlation.