## 3 Results
### Classification \"Traffic\"  
The classification outcomes for the \"traffic\" attribute illustrate the capabilities of different machine learning models to discern traffic congestion levels. The random forest (RF) classifier shows exemplary performance, boasting an accuracy of 98.80%. It exhibits high precision and recall across all traffic conditions, with the recall for high congestion conditions at 96.64%, low congestion conditions at an impressive 99.87%, and normal congestion conditions at 94.65%. The specificity for each class is also notably high, with the low congestion condition achieving almost perfect specificity at 99.95%. The decision tree (DT) classifier, while slightly less accurate at 97.58%, still maintains strong precision and recall, with a recall of 95.64% for high congestion conditions and 98.53% for reinforcing the DT classifier's reliability in accurately identifying traffic conditions. The K-nearest neighbors (KNN) classifier shows an accuracy of 95.75%, with the recall for the high congestion condition at 94.13%, low congestion condition at 97.49%, and normal congestion condition at 87.31%. The specificity is high for both the high congestion condition and the normal congestion condition, indicating a strong true negative rate. Show Table 4. Lastly, the ensemble model using a voting classifier achieves an accuracy of 97.14%. The model presents a high level of precision and recall, with the recall for the high congestion condition at 94.80%, the low congestion condition at 98.93%, and the normal congestion condition at 88.99%. The specificity values for the classes are also very high, especially for normal congestion conditions, at 99.38%. In summary, all models demonstrate high effectiveness in classifying traffic conditions. The random forest classifier stands out with the highest overall accuracy and recall, suggesting its superior performance in this classification task. The ensemble voting classifier also has strong results, which shows how important it is to combine several models to obtain high accuracy and a good balance of recall and specificity in a variety of traffic conditions. ow congestion conditions, and 93.88% for normal congestion conditions. The specificity for these classes is robust; Figures 10 and 11 depict the contribution of various features to the predictions made by a random forest classifier for two distinct samples, as analyzed by the tree interpreter tool. In Figure 10, the horizontal bar chart shows the feature contributions for Sample 21. Each bar represents the magnitude of a feature's influence on the model's prediction, with blue bars indicating a positive contribution and red bars denoting a negative contribution. For this sample, \"Vehicle Speed Instantaneous\" and \"Fuel Consumption Average\" make the most significant positive contributions, suggesting they are highly influential in the classifier's prediction towards the positive class. Meanwhile, features such as \"Engine Load\" and \"Manifold Absolute Pressure\" exhibit the most notable negative contributions, implying they steer the prediction away from the positive class. For Sample 20, Figure 11 provides a similar analysis. Once again, we see a combination of positive and negative contributions across different features. Notably, \"vehicle speed average\" shows a substantial positive contribution, while \"altitude variation\" and \"vehicle speed variance\" have strong negative impacts on the classifier's prediction. These \begin{table}
\begin{tabular}{c c c c c} \hline
**Model** & **Accuracy** & **Recall (High)** & **Recall (Low)** & **Recall (Normal)** \\ \hline RF & 98.80\% & 96.64\% & 99.87\% & 94.65\% \\ \hline DT & 97.58\% & 95.64\% & 98.53\% & 93.88\% \\ \hline KNN & 95.75\% & 94.13\% & 97.49\% & 87.31\% \\ \hline Voting Classifier & 97.14\% & 94.80\% & 98.93\% & 88.99\% \\ \hline \end{tabular}
\end{table} 
Table 4: Comparison of machine learning models for “Traffic” classification.