## 4 Analysis of the Numerical Simulation and Experimental Results
### Experimental Results and Simulation Analysis  
#### 4.2.1 Analysis of the Optimized Deployment Model  
In this section, MATLAB is used to visualize the model. It can be seen from Figure 5a that the curve L1 is the peak curve of the function. As the traffic density on the expressway increases, the peak curve closes upon the plane with zero permeability, indicating that the higher the traffic density is, the lower the permeability required to reach the peak. The curve L2 is the threshold curve and when the point on the surface is above the curve it represents the income of the model is positive, as in Figure 5b (S1). When the point on the surface is below the curve, the income of the model is negative, as in Figure 5b (S2). The curve L3 represents the state curve when the linkage between vehicles starts to form and the permeability required for the state curve to reach its peak decreases as the traffic density increases. From Figure 5b, it can be seen that when the point lies within S3, no linkage will be formed between vehicles.  
Taking the surface slice of the model at high traffic density, as shown in Figure 6a, there are three key nodes, namely a, b, and c, on the curve. When the permeability before node a is low, the income is mainly from negative deployment costs. After node a, the vehicle begins to form a connection, at which point the positive income from roadside RSU information coverage increases as the number of intelligent connected vehicles increases until it reaches a peak at point b. However, with the increase in permeability, the total energy consumed by information transmission between nodes is also increasing, which inhibits the growth of the curve. When reaching point c, the positive income is equal to the negative income and the model is in equilibrium.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter Value** & **Value** & **Illustrate** \\ \hline \(R\) & 200 m & Roadside RSU communication radius \\ \(\tau\) & 60 s & Information life cycle \\ \(R_{v}\) & 100 m & Intelligent connected vehicle perception range \\ \(\tau_{a}\) & 0.36 s & Instantaneous transmission time \\ \(\beta_{n}\) & 0.33 & Proportion of three types of ferry transmission \\ \(l\) & 1000 Bytes & Packet size \\ \(e_{fx}\) & 10 pj & Circuit energy loss amplification coefficient \\ \(\epsilon_{mp}\) & 0.013 pj & Circuit energy loss amplification coefficient \\ \(\gamma\) & 5 & Roadside RSU unit cost \\ \(\eta\) & 25 & Network cost \\ \(E_{eloc}\) & 50 nj & Initial node energy \\ \(v_{i}\) & 100 km/h & speed of the vehicle carrying the information \\ \(\Delta v_{i}\) & 20 km/h & chasing speed \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameter settings.  
Figure 5: Three-dimensional surface of the P-P model.  
The surface slice of the model at low traffic density is taken and shown in Figure 6b. Compared with a in the figure there are only two key nodes on this curve and both of them are less than zero, which are a and b. Under this traffic density, no matter how the permeability increases, the deployment cost of the model will be negative, at which time there is no optimal deployment spacing.