## 3 Results and Discussion
### Model Results and Validation
#### 3.2.2 Model Results--Zone 4  
Table 6 summarizes the performance of each model on Zone 4, using the same LOOCV as for Zone 3:  
Once again, KNN delivers the best results, achieving the highest R\({}^{2}\) (\(\approx\)0.72) and the lowest MAE (\(\approx\)0.53 h) and RMSE (\(\approx\)0.69 h). Figures 14 and 15 show the actual vs. predicted and residual plots, respectively, confirming that KNN predictions align closely with observed values across the range of durations. Compared to the baseline (MAE = 1.17 h, RMSE = 1.31 h), the KNN model achieves an absolute reduction of 0.64 h in MAE and 0.61 h in RMSE. Notably, the reduction in RMSE is almost equal in magnitude to the MAE drop, despite RMSE giving more weight to larger errors. This suggests that KNN not only improves average predictive accuracy but also effectively reduces the impact of large deviations, resulting in a more reliable and stable model output. As for the rest of the models, the linear perform similarly (R\({}^{2}\approx\) 0.65), indicating that while a global linear trend captures part of the behavior, it fails to account for important non-linear patterns in the data. Meanwhile, the Decision Tree model exhibits poor generalization: its low R\({}^{2}\) and high RMSE point to overfitting, even with minimal depth. Taken together, these findings confirm KNN as the most appropriate model for Zone 4. Regarding explainability, the final KNN model for Zone 4 was also examined using permutation importance. In this case, the Fuel Intensity Index from the previous zone emerged as the most relevant input, with a mean contribution of 0.77 to the R\({}^{2}\) score (Figure 16). In contrast, the simulated soot content showed a lower importance (0.20) and greater variability across permutations, suggesting a less consistent influence. Nevertheless, following the same rationale as in Zone 3, this feature was retained in the model due to its physical significance and its role in facilitating transparent communication with maintenance stakeholders. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **R\({}^{2}\)** & **MAE** & **RMSE** & **Parameters** \\ \hline Baseline & 0.0000 & 1.1714 & 1.3074 & \\ Linear Regression & 0.6481 & 0.7117 & 0.7756 & \{\} \\ Ridge Regression & 0.6479 & 0.7119 & 0.7758 & \{’alpha’: 0.01\} \\ Decision Tree & 0.3241 & 0.7665 & 1.0748 & \{’max_depth’: 2, ’min_samples_split’: 2\} \\ KNN & 0.7198 & 0.5340 & 0.6921 & \{’n_neighbors’: 3, ’weights’: ‘uniform’\} \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance metrics of regression models for predicting duration in Zone 4. Figure 13: Impact of feature permutation on model performance—Zone 3. Moreover, the R\({}^{2}\) value dropped below zero (\(-0.10\)), indicating that the model failed to explain any variance in the new data and performed worse than the naive baseline. This degradation highlights a clear loss of generalization capacity when applied to data from a subsequent operational period. Several factors may explain this abrupt drop in performance. First, changes in operating patterns, such as the inclusion of routes that were not present in the initial dataset, shifts in traffic conditions, or variations in driving behavior, may have altered the statistical properties of the input features, leading to a phenomenon known as data drift. In this context, data drift occurs when the real-world data distribution diverges from that used to train the model, causing it to make inaccurate predictions despite being valid at the time of deployment. Second, the model's dependence on a very small number of neighbors (k = 3) may have made it overly sensitive to local patterns present in the training data. This situation reveals a high level of sensitivity in the developed model, where slight changes in the input distribution can significantly degrade predictive performance. Such sensitivity reflects a lack of robustness to perturbations in the feature space and to variations in local data structure.