## 1 Introduction
### Prediction Experiment of Optimal Configuration of Plant Landscape  
#### 2.3.1 Influencing Factors  
The \"Code for Planting Planning and Design of Urban Roads\" [17] in China has established corresponding standards for the landscape planning and design of roads, considering aspects such as landscape effect, traffic safety, plant planting, and plant maintenance. The \"Code\" primarily specifies regulations for the color, height, roadside distance, and plant spacing of plant landscapes. It stipulates that the width of the greenbelt on both sides should be 1.5 m or wider. The spacing of street trees should not exceed five times the crown width, with a minimum planting distance of 4 m. Additionally, the minimum distance between the center of the trunk of a street tree and the outside of the curb should be 0.75 m. Greenery is generally dominated by evergreen plants, mainly trees. However, the \"Code\" does not provide specific research on the plant landscape of various sections of the road. The 'Code' [17] mainly gives brief regulations on the height, spacing, distance from the roadside, and color design of the street landscape. Therefore, this study identified four influencing factors of road landscape, i.e., height, plant spacing, roadside spacing, and \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & **Min (km/h)** & **Max (km/h)** & **Mean (km/h)** & **Standard Deviation** \\ \hline Real vehicle data 1 & 0.16 & 0.98 & 0.57 & 2.8 \\ Simulated data 1 & 0.12 & 1.01 & 0.565 & 3.2 \\ Real vehicle data 2 & 0.13 & 1.31 & 0.72 & 4.1 \\ Simulated data 2 & 0.35 & 1.42 & 0.885 & 3.8 \\ Real vehicle data 3 & 0.26 & 1.21 & 0.735 & 3.9 \\ Simulated data 3 & 0.31 & 1.36 & 0.835 & 3.6 \\ Real vehicle data 4 & 0.18 & 1.20 & 0.69 & 3.1 \\ Simulated data 4 & 0.42 & 1.33 & 0.875 & 3.4 \\ Real vehicle data 5 & 0.46 & 1.10 & 0.78 & 2.9 \\ Simulated data 5 & 0.26 & 1.22 & 0.74 & 3.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Statistical analysis table of lateral displacement for real vehicle experiments and simulated experiments. Figure 5: The lateral displacement curves of five groups of real vehicle experiments and simulated driving experiments. color, and analyzed and investigated these four factors. Finally, this study quantified the range of the four influencing factors in the plant landscape. To better select the levels of the factors, the color factor was quantified. [PERSON] (2018) and others studied the impact of the brightness level of plant colors on drivers' psychological changes. They processed the plant landscape in the driver's field of view in grayscale and obtained the brightness level of the plant landscape. The research results showed that a brightness level of 3-5 could effectively alleviate drivers' tension. Therefore, we limited the color levels based on the brightness level to the range of 3-6, as shown in Table 3.  
In the'specification' (Kip  
The variables in the equation are defined as follows: Y represents the predicted value: \(X_{i}\) denotes the value of the influencing factor; \(\beta_{0}\) is the constant term; \(\beta_{i}\) represents the linear coefficient; \(\beta_{ij}\) signifies the interaction coefficient; \(\beta_{ii}\) represents the quadratic coefficient; K is the number of factors; and \(\epsilon\) denotes the error term. The BBD response surface methodology can be implemented using Design Expert v. 10 software, which facilitates a series of analyses related to experimental design. In comparison to traditional software, Design Expert 10 offers advantages such as user-friendly operation and a simple interface and does not necessitate users to possess an in-depth understanding of mathematical statistics. By employing this software to design experiments, one can derive the optimal solution, establish mathematical models, and conduct analyses. The process primarily involves three major steps, i.e., experimental design, regression equation analysis, and optimal result prediction.