## 3 Results
\begin{table}
\begin{tabular}{l l l l} \hline \hline  & \multicolumn{1}{c}{**Tiredness Score**} & \multicolumn{1}{c}{**Distance to**} & \multicolumn{1}{c}{**Driver Was Selected for**} \\  & & **Departure Point** & **the Transport Order** \\ \hline Min & 0 & 0 & \\
1 st interquartile range & 0 & 71.51 & \\ Median & 7.02 & 98.32 & 2571 (19.85\%) \\
3 rd interquartile range & 16.98 & 149.76 & \\ Max & 61 & 310.2 & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Summary of the parameters of the test set. The ROC curves of the RF classifier for both the training and test sets can be visualized in Figure 2.  
After obtaining the MLP model, we displayed it in the RStudio console, in a similar manner as we did with the RF classifier, and we obtained the following result. This result also demonstrates the main parameters and training performance of the model (10-fold cross validation, 10 repeats, preprocessing modes, one hidden layer containing five neurons, and accuracy and kappa values for the different numbers of neurons in the hidden layer):  
Multi-Layer Perceptron, with multiple layers  
2 predictor  
2 classes: '0', '1'  
Pre-processing: scaled (2), centered (2)  
Resampling: Cross-Validated (10 fold, repeated 10 times)  
Additional sampling using SMOTE prior to pre-processing  
Resampling results across tuning parameters:  
layer1 Accuracy Kappa  
1 0.8107440 0.4717333  
3 0.8100957 0.4679649  
5 0.8390885 0.5161155  
Tuning parameter 'layer2' was held constant at a value of 0  
Tuning parameter 'layer3' was held constant at a value of 0  
Accuracy was used to select the optimal model using the largest value. Figure 2: ROC curves obtained by the RF model on the training and test sets. The final values used for the model were layer1 = 5, layer2 = 0 and layer3 = 0. The performance metrics on the training and test sets for the MLP model are illustrated in Table 7.  
The ROC curves of the MLP classifier for both the training and test sets are illustrated in Figure 3.  
As can be seen from the comparison of the RF versus MLP models, the RF classifier achieved higher performance metrics (accuracy, AUC) on both the training and test sets. Therefore, the RF model was chosen to be integrated into the platform. \begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{2}{c}{**Multi-Layer Perceptron**} \\ \cline{2-3}  & **Train Set** & **Test Set** \\ \hline Accuracy & 85\% & 86\% \\ \hline
95\% CI & (0.8346, 0.8637) & (0.8278, 0.8852) \\ \hline \(p\) value & 0.045 & 0.012 \\ \hline Sensitivity & 86\% & 89\% \\ \hline Specificity & 78\% & 73\% \\ \hline PPV & 95\% & 94\% \\ \hline NPV & 53\% & 58\% \\ \hline AUC & 0.8994 & 0.8857 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The performance metrics of the MLP model on the training and test sets. Figure 3: ROC curves obtained by the MLP model on the training and test sets. For each transport order, the driver that is available and has the highest probability of being chosen by the RF model, is selected to be recommended to the dispatcher for assignation to the transport order. In order to exemplify the practical use of our model, we present in Table 8 two real-world use cases/scenarios. For the transportation order scheduled on 1 June 2022 at 00:36, destined to depart from PLOIESTI VEST to BASARABI, the dispatcher manually assigned as driver: anonymized driver 4. The platform was run, and it also recommended as driver: anonymized driver 4. Therefore, the platform decision matched the human decision. This proves that we achieved our goal to develop an automatic module that could replace the manual work of assigning drivers to a locomotive, that is nowadays performed by the dispatcher.