## 4 Discussion  
The Ferodata AI optimization module retrieves data from the database, performs a forecast regarding the most suitable locomotive driver to be allocated for expeditions considering the location of the driver, rest time, and availability. It should be considered that, as new data are gathered, the model will undergo retraining and retesting in order to improve its performance. The locomotive driver serves as a vital link within the rail transport chain and holds an integral role in its operations [21]. Nevertheless, this profession has encountered a range of challenges in recent years [22]. Factors such as a persistent shortage of qualified drivers, mounting stress levels, and deteriorating working conditions have arisen [23]. As a result, there exist substantial opportunities for innovating the organization of this job position [24]. Our study aimed to propose an effective and innovative method to improve the management of this job position, in the current context. To our knowledge, there has been no scientific paper published to date that proposed a random forest model for automatic allocation of drivers on rail freight transportation orders. The model proposed by us obtained excellent performance metrics on the train set (accuracy: 99%, AUC: 0.9927) and very good and encouraging performance metrics on the test set (accuracy: 88%, AUC: 0.8998). With the increase in the size of the database and access to more variables (driver skills, driver preferences, enroute change of drivers, etc. ), the model will be retrained and improved. The ML model proposed by us provides several valuable contributions to the logistics of rail freight transportation industry: optimized resource allocation, reduced human error, cost savings (minimize labor cost), real-time adaptability, scalability, and ensuring compliance with industry regulations, safety standards, and labor laws. Consistent scientific evidence shows that the primary outcome of economic significance would be the reduction in costs through the efficient utilization of train resources, particularly train personnel, including vehicle drivers [25]. Efficient driver assignment through automation can lead to cost savings by reducing overtime expenses, optimizing resource utilization, and minimizing disruptions due to unplanned driver unavailability [26]. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Scenarios**} & **Date of Scheduled** & **Depature Point** & **Destination** & **The Driver Manually** & **The Driver** \\  & **Transportation Order** & **Point** & **Assigned by the** & **Recommended by** \\  & & & & **Dispatcher** & **Our Platform** \\ \hline \multirow{2}{*}{Scenario 1} & \multirow{2}{*}{1 June 2022, 00:36} & PLOIESTI VEST & BASARABI & \multirow{2}{*}{Anonymized driver 4} & \multirow{2}{*}{Anonymized driver 4} \\  & & Result: The platform decision matched the human decision. & & \\ \hline \multirow{2}{*}{Scenario 2} & \multirow{2}{*}{31 May 2022, 17:14} & GHERLA & AIUD & \multirow{2}{*}{Anonymized driver 12} \\  & & Result: The platform decision matched the human decision. & & \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Illustration of two real-world use cases. It also allows for better planning of driver schedules and potential reduction in the need for additional staff [27]. Automation allows for the optimization of driver assignments based on various factors such as availability, and rest requirements. This leads to more efficient utilization of resources and reduces idle time or unnecessary delays in the transport process [28]. Automated optimization helps in maximizing productivity by ensuring that drivers are consistently engaged in transporting freight. This results in improved overall efficiency, better resource allocation and a balanced workload distribution in the rail freight operations, preventing underutilization or overburdening of drivers. The planning and execution processes can be simplified by handling the complex task of driver assignment.