## 6 Discussion  
In summary, a comparison of the logistic regression models for each driving mode was conducted (Table 15). In all models, the statistically significant variables were the vehicle velocity, relative engine load, and relative throttle position. Additionally, for the models assessing urban and highway driving, the significant variables included engine power and torque.  
In the analyzed case, the impact of selected variables on the probability of eco-friendly driving was examined, specifically for cases where CO2 emissions are less than 200 g/km. For each model, an increase in driving velocity of 1 km/h increases the likelihood of eco-friendly driving by 23%, 16%, and 15% during vehicle start-up, in urban mode, and on the highway, respectively. A similar situation occurs with engine torque, where an increase of 1 Nm raises this probability by 3% and 7% for urban driving and highway driving, respectively.  
It is important to note that with increases in the values of the relative load and engine power variables, the probability of driving with emissions below 200 g/km decreases. Specifically, an increase in relative engine load by 1% results in a decrease in probability by 9%, 15%, and 28% (for vehicle start-up, urban driving, and highway driving, respectively).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Explanatory Variable**} & **Odds Ratio of the Parameter** & \multirow{2}{*}{**Odds Ratio of the Parameter**} & \multirow{2}{*}{**Odds Ratio of the Parameter**} \\  & **Model During Vehicle** & & \\  & **Startup** & & \\ \hline Vehicle velocity & 1.15 & 1.13 & 1.16 \\ Relative engine load & 0.72 & 0.71 & 0.73 \\ Relative throttle position & 0.73 & 0.64 & 0.83 \\ Engine power & 1.07 & 1.04 & 1.10 \\ Torque & 1.17 & 1.16 & 1.19 \\ \hline \hline \end{tabular}
\end{table}
Table 15: Comparison of odds ratios for variables in each logistic regression model.  
Figure 10: ROC curve for the training set for the highway driving model.  
Furthermore, an increase in engine power by 1 kW leads to a decrease of 18% and 27% (for urban and highway driving, respectively).  
It should be emphasized that, for the variable representing the relative throttle position, an increase of 1% reduces the probability of eco-friendly driving by 39% and 19% during vehicle start-up and in urban mode, while in highway driving, it increases the probability by 17%.  
Finally, the values of indicators characterizing the goodness of fit of the constructed models were calculated, and the results are summarized in Table 16.  
The logistic regression model for vehicle start-up (Table 16) shows the lowest sensitivity, approximately 82%, among all considered models, along with an accuracy level of around 89%. However, a precision of about 85% indicates that the model has a moderately effective capability in eliminating false alarms (FN), which is extremely beneficial from the perspective of monitoring CO\({}_{2}\) emissions [48].  
In the case of urban driving (Table 16), the values of the discussed parameters reach significantly higher levels, with sensitivity at around 96%, precision at about 92%, and accuracy at 93%. These results indicate a high effectiveness of the developed model in urban environments, which may stem from more diverse driving conditions [48].  
On the other hand, the logistic regression model related to highway driving (Table 16) achieves the highest values for goodness-of-fit parameters among the created models, with sensitivity at around 97% and precision at about 93%, falling slightly short only in terms of accuracy, which is approximately 92%, compared to the urban driving model. The high efficiency of this model is attributed to the more stable driving conditions on highways than in urban driving, which greatly facilitates the classification process.  
The best accuracy, i.e., the percentage of correctly classified observations, is characteristic of the urban driving model (about 93%), while the lowest accuracy is observed in the vehicle start-up model (about 89%).