## 5 Experimental Results and Analysis
### Experimental Study on Speed Prediction of Section Vehicles Based on WT-LightGBM
#### 5.2.2 Result Analysis  
In order to verify the effectiveness and superiority of our proposed WT-LightGBM model in predicting the speed of vehicle sections, the experiment selected transaction data generated by ETC frames of three sections (LD) of Fujian Province's expressway on 1 May 2021 and 1 June 2021, for speed prediction experiments. Among them, LD\({}_{1}\) is the section with no service area for normal traffic flow on weekdays, LD\({}_{2}\) is the section with service area for normal traffic flow on weekdays, and LD\({}_{3}\) is the section with more traffic flow on holidays, and for sections with service areas, each section includes five sections. The experimental results are shown in Figure 10. It can be clearly seen from the figure that the prediction results of the WT-LightGBM model are significantly better than those of the LightGBM model in any road segment. Among them, under the normal traffic flow and service area of LD\({}_{2}\), the RMSE and MAE of model WT-LightGBM are 12.46% and 17.63% lower than LightGBM, respectively, and the R\({}^{2}\) of model WT-LightGBM is 3.98% higher than LightGBM. This is because the LightGBM model did not consider the noise of the data, resulting in low prediction accuracy. It also proves that it is feasible to use WT-LightGBM to improve the accuracy of the prediction. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter Name** & **Value** \\ \hline learning\_rate (step size per iteration) & 0.1 \\ n\_estimators (number of iterations) & 470 \\ num\_leaves (number of leaf nodes) & 31 \\ max\_depth (the maximum depth of the tree) & 7 \\ Subsample (random sampling rate of samples per tree) & 0.79 \\ subsample\_freq (sampling frequency) & 8 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Model parameter values. Figure 10: Comparative experiment between WT-LightGBM and LightGBM. In order to verify the reliability of model WT-LightGBM, this paper also applies several different machine learning and deep learning models to test the data of these three road sections, including support vector regression (SVR), k-nearest neighbor (KNN), gate recurrent unit (GRU), random forest (RF), and XGBoost. Analyze the difference between the predicted value and the actual value, and calculate the RMSE, MAE, and R\({}^{2}\) of the prediction model. The prediction performance evaluation is shown in Tables 9-11. Observing the data in Table 9, it can be seen that model WT-LightGBM performs better than other models in the experiment, with the R\({}^{2}\) score of 0.95, and RMSE and MAE of 3.6233 and 2.7256, respectively. Model WT-LightGBM can effectively handle large-scale high-dimensional data and maintain high robustness in the presence of noise or large fluctuations in the data. These characteristics make the WT-LightGBM model highly practical and widely adaptable in predicting vehicle traffic speeds. The SVR model may have poor performance in processing big datasets or multi-feature datasets, while ETC data has more features. Although the KNN model is simple and easy to use, it has high computational complexity on the big dataset. Although GRU can process sequence data, there may be overfitting issues for this problem, with the R\({}^{2}\) score of 0.8156. RF exhibits strong generalization ability, with the R\({}^{2}\) score of 0.9276, and has lower RMSE and MAE. The advantage of RF is that it can handle high-dimensional data, but its training time is relatively long. XGBoost and LightGBM are both GBDT models that perform well on many problems. The R\({}^{2}\) score of XGBoost reached 0.9075, but in comparison, LightGBM performed even better. LightGBM has the characteristics of fast speed and efficient memory utilization, which are especially suitable for processing large-scale data. The WT-LightGBM model we propose is an optimized model for LightGBM, which uses wavelet denoising before data processing to eliminate section speed noise and preserve original data features, improving prediction accuracy and reducing errors.