## 5 Experimental Results and Analysis
### PTV Quantitative Evaluation Algorithm
#### 5.3.3 Result Analysis  
PTV detection is a multi-dimensional evaluation process that not only considers the risk level of individual indicators, but also comprehensively considers the impact of each indicator on the overall driving process. To verify the effectiveness of the model, experimental data was obtained from 12,274 vehicles during the busy holiday season in Fuxia, Fujian Province, in 2021. The historical traffic speeds of 12,274 vehicles were calculated, and the WT-lightGBM model was used to predict the driving speed of the next section (as the driving section). Then, the PTV evaluation algorithm was used to assess the threat of the vehicles. Based on the above data, the experiment used accuracy and recall to evaluate the effectiveness of the model, and the calculation formula is as follows.  
* Accuracy \[\text{ACC}=\frac{\text{TP}+\text{TN}}{\text{TF}+\text{TN}+\text{FP}+\text{ FN}}\] (21)
* Recall \[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}}\] (22)  
where TP indicates that the prediction is positive and correct; TN indicates that the prediction is negative and correct; FP indicates that the actual negative class is classified into the positive class; FN indicates that the actual positive class is classified into the negative class. The accuracy rate reflects the ratio of the number of samples correctly classified by  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Element** & \(\mathbf{f_{1}}\) & \(\mathbf{f_{2}}\) & \(\mathbf{f_{3}}\) & \(\mathbf{f_{4}}\) \\ \hline f1 & 1 & 3 & 4 & 3 \\ \hline f2 & 1/3 & 1 & \(\frac{1}{2}\) & 2 \\ \hline f3 & \(\frac{1}{4}\) & 2 & 1 & 2 \\ \hline f4 & 1/3 & \(\frac{1}{2}\) & \(\frac{1}{2}\) & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 13: PTV indicators judgment matrix.  
the classifier to the total number of samples for a given test dataset. The results are shown in Table 14, and it can be seen from the table that the accuracy and recall of this model are 98.03% and 99.55%, respectively. The results indicate that the proposed algorithm can effectively detect vehicles with potential hazards.  
In addition, due to the inevitable subjectivity and uncertainty in PTV assessment, we converted the assessment results from qualitative classification to threat level classification to more effectively measure the threat degree of PTVs. To illustrate our research findings, this article proposes a potential threat index for quantitative evaluation of PTVs, which distinguishes potential PTVs based on information from a large amount of ETC data. In order to clearly express the characteristics of high-risk vehicles, we determined three reference values for describing PTVs with different threat levels by calculating the membership values of all vehicles, namely no threat, low threat, moderate threat, and high threat. The classification criteria for threat levels are based on the calculation results of membership values, which is also an innovative method proposed in this article for quantifying PTV threat levels. The specific results are shown in Table 15 below:  
By using this novel PTV threat level quantification method, we can more accurately assess the potential threat of vehicles in transit and provide an important reference for traffic management and safety decision making, which has certain application prospects. In order to better validate the effectiveness and reliability of this method, a comparative analysis was conducted on the PTVs detected by this method. Some PTVs are shown in Table 16. The * in the table was desensitized due to the privacy concerns of the data used in our experiment.