## 3 Simulation Experiments
### Simulation of SVR Short-Time Traffic Flow Prediction Model Based on Associated Roads
#### 3.3.2 Analysis of SVR Short-Term Traffic Flow Prediction Model  
After road correlation for each road segment, the SVR model was established with a penalty parameter C of 1.2466 and a kernel parameter gamma of 0.8327 based on the short-term traffic flow data of the target road as well as the correlated roads as the database. Taking the data on 1 April 2022 as an example, some of the model's prediction results for trunk road roads are shown in Figure 12, and some of the short-term traffic flow prediction results for feeder roads are shown in Figure 13.  
This is a bar chart of the change in the numerical value of traffic flow on different roadways between 11:00 and 11:50. The graph shows a gradual increase in values for all road sections, but the increase varies. The band * shows the prediction of SVR for the associated roads, and it can be seen that the model has high accuracy and stability in predicting traffic flow on different roads, can adapt to changes in traffic flow in real time, and demonstrates good applicability on different types of roads. Although there are slight differences in some specific time periods, overall, the SVR model provides an effective prediction tool for traffic management and planning with practical application value, while there is still room for further optimization. The prediction results of twenty roads of the SVR short-time traffic flow prediction model for associated roads were analyzed, and the final result was that the R\({}^{2}\) of the overall model was 0.9261, the R\({}^{2}\) of the ten main roads was 0.9395, and the R\({}^{2}\) of the ten branch roads was 0.9127. These R\({}^{2}\) values indicate the proportion of the variance in traffic flow that is predictable from the model, with higher values suggesting a better fit of the model  
Figure 12: Prediction results of selected trunk roads based on SVR mode. Note: * are SVR predictions for associated roads. Figure 13: SVR model-based road prediction results for selected feeder roads. Note: * are SVR predictions for associated roads. to the data. The overall model's high R\({}^{2}\) value of 0.9261 suggests that it can explain 92.61% of the variability in traffic flow across all roads considered. The slightly higher R\({}^{2}\) value for main roads (0.9395) compared to branch roads (0.9127) indicates that the model performs slightly better on main roads, likely due to the more consistent traffic patterns and higher data availability on these roads. The other results of the evaluation indexes are shown in Table 8:  
R\({}^{2}\) (coefficient of determination): R\({}^{2}\) is a statistical measure that represents the proportion of the variance for a dependent variable that is explained by an independent variable or variables in a regression model. In the context of traffic flow prediction, a higher R\({}^{2}\) value indicates that a larger proportion of the variability in traffic flow can be explained by the model. An R\({}^{2}\) value of 0.9261 for the overall model means that 92.61% of the variability in traffic flow across all roads is predictable by the model. The slightly higher R\({}^{2}\) value for main roads (0.9395) compared to branch roads (0.9127) suggests that the model fits the data of main roads slightly better, likely due to more consistent traffic patterns and higher data availability on main roads. D-W tests (Durbin-Watson test): The Durbin-Watson test is a statistical test used to detect the presence of autocorrelation (a relationship between values separated from each other by a given time lag) in the residuals from a regression analysis. The test statistic ranges from 0 to 4, with values close to 2 indicating no significant autocorrelation. Values significantly less than 2 indicate positive autocorrelation, while values significantly greater than 2 indicate negative autocorrelation.