## 7 Conclusions  
The logistic regression model can be used to create models that classify levels of CO\({}_{2}\) emissions in vehicles with internal combustion engines based on real operational data. Integrating the discussed CO\({}_{2}\) classification models with the available vehicle information systems can lead to increased environmental awareness among drivers and promote more eco-friendly driving styles.  
These classification models, along with built-in onboard systems, could provide drivers with real-time recommendations regarding CO\({}_{2}\) emissions based on the current driving mode. For example, they could suggest minimizing abrupt velocity changes that lead to increased emissions (during vehicle start-up), optimizing velocity to maintain smooth driving (in urban driving), and maintaining a constant velocity (on highways). Such solutions would enable the average driver not only to exert a specific impact on environmental protection but also to reduce the operational costs of their vehicle.  
The created logistic regression models exhibit varying effectiveness depending on the driving mode, achieving the best results--i.e., the highest values of sensitivity, precision, and accuracy--during urban driving and highway driving, and the lowest during the vehicle start-up cycle. In the context of vehicle start-up, the unstable operating conditions of the engine significantly hinder the classification of emission levels, resulting in the aforementioned lower efficiency of the models in this cycle.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Sensitivity** & **Precision** & **Accuracy** \\ \hline Vehicle startup & 81.88\% & 84.70\% & 88.74\% \\ Driving in urban mode & 95.58\% & 91.54\% & 93.33\% \\ Driving on the highway & 96.62\% & 93.33\% & 92.30\% \\ \hline \hline \end{tabular}
\end{table}
Table 16: Comparison of goodness of fit in different logistic regression models.  
During vehicle start-up, the most significant factors affecting the probability of eco-friendly driving (i.e., CO2 emissions below 200 g/km) are vehicle velocity, relative engine load, and the relative position of the throttle. In urban and highway driving, the list of parameters also needs to include engine power and torque. Furthermore, the statistical analysis revealed that engine speed in RPM does not significantly impact CO2 emissions at the assumed significance level of \(\alpha=0.05\).  
The problem encountered in this work is the limited sampling frequency of some data (resulting from sensor limitations, e.g., vehicle speed), which may result in the inability to capture important characteristics, especially in the case of a short-term start-up phase of the driving cycle. Directions for further research may include the application of other machine learning methods, e.g., decision trees, support vector machines (SVM), random forests, or neural networks in the considered context in search of algorithms providing the highest values of classification goodness of fit indicators.  
Conceptualization, M.R. and M.G.; methodology, A.B.; software, M.R. and M.G.; validation, M.R., A.B. and M.G.; formal analysis, M.R., A.B. and M.G.; investigation, M.R. and M.G.; resources, M.R. and M.G.; data curation, M.R.; writing--original draft preparation, M.R. and M.G.; writing--review and editing, M.R. and A.B.; visualization, M.R., A.B. and M.G.; supervision, M.R., A.B. and M.G.; project administration, A.B.; funding acquisition, A.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding authors.  
The authors declare no conflicts of interest.