## 4 Results and Discussion
### Phase 1: Analysis of the current situation
Units: Minutes per vehicle (min/veh). \[T_{operation}=\frac{T_{total}}{N_{vehicles}}\]  
Calculation of the reduction in waiting times. This equation measures the reduction in waiting times achieved by implementing IoV and automation. Unit: Minutes (min). \[\Delta T_{waiting}=T_{manual}-T_{automated}\]  
* Calculation of Vehicle Flow: \[ \begin{array}{rl}\odot&\text{Calculates the number of vehicles processed per hour.}\\ \odot&\text{Units: Vehicles per hour (veh/h).}\\ \odot&\text{C}_{fuel}\text{: Fuel consumption per vehicle (litres/vehicle).}\\ \odot&\text{EC}_{\text{O}_{2}}\text{: CO}_{2}\text{ emissions per litre of fuel (kg/L).}\\ \odot&\text{Units: Kilograms (kg).}\\ \end{array} \]
* CO\({}_{2}=N_{vehicles}\times\left(C_{fuel}\times E_{\text{CO}_{2}}\right)\)
* CO\({}_{2}\) Emissions Calculation. Units: Kilograms (kg). \[\text{CO}_{2}=\text{Consumo Promedio (litros/min)}\times\Delta T_{expera}\times\text{Emissions per litro (kg CO${}_{2}$/litro)}\]
* Hourly Cost Calculation: \[C_{hourly}=\left(C_{tabor}+C_{energy}\right)\times T_{operation}\]
* CO\({}_{\text{Labor}}\): Labor cost per vehicle (EUR). \[ \begin{array}{rl}\odot&\text{C}_{\text{energy}}\text{: Energy cost per vehicle (EUR).}\\ \odot&\text{Units: Euros per hour (EUR/h).}\\ \end{array} \]
* Maximum terminal capacity: \[C_{max}=\frac{N_{vehicles}}{T_{operation}}\]
* Description: Defines the maximum number of vehicles that can be processed under ideal conditions. \[ \begin{array}{rl}\odot&\text{Units: Vehicles per hour (veh/h).}\\ \end{array} \]
* Reduction in CO\({}_{2}\) emissions: Based on reduced waiting times and fuel consumption per vehicle. \[ \begin{array}{rl}\odot&\text{CO}_{2,manual}\text{: CO}_{2}\text{ emissions in manual operations.}\\ \odot&\text{CO}_{2,automated}\text{: CO}_{2}\text{ emissions in automated operations.}\\ \odot&\text{Units: Kilograms (kg).}\\ \end{array} \]* Economic savings calculation: \[Savings=Cost_{waiting}\times\Delta T_{waiting}\times Vehicles\]  
Next, a typical scenario is developed to provide a clear picture of how the study develops. _Scenario A: Optimization of vehicular traffic in waiting areas._  
Initial data:  
* Tactual: 20 min
* Tcon IoV: 15 min
* Average consumption: 4 L/h
* Emissions per litre: 2.64 kg/L
* Vehicles processed: 300 vehicles/day
* Vehicles processed: 350 vehicles/day
* Reduction in waiting times: \(\Delta\)Twair = 20\(-\)15 = 5 min
* Reduction in emissions: CO\({}_{2}\) reduced = 4 \(\times\) 560 \(\times\) 2.64 = 0.88 kg CO\({}_{2}\)
* Economic savings: Savings = 3 \(\times\) 5 \(\times\) 300 = EUR 4500/day
* Increase in flow: 16.7%
* Reduced emissions: 0.88 kg CO\({}_{2}\)/vehicle  
**Phase 3: Implementing the calculations**  
Once the work scenario has been determined and the values have been assigned to the variables, the calculations of the formulas are carried out and the following values are obtained (Table 2):  
An example of how the calculations of the total economic savings have been made, which correspond to the savings generated by the reduction of waiting times and operating costs, per hour of operation, considering the number of vehicles processed in an hour, is shown below. Assumptions:  
* Terminal operates 16 h per day. * It operates 250 days a year. * Average hourly savings: EUR 150 per hour. * Daily Calculation: EUR 2400 savings per day
* Annual Calculation: EUR 600,000  
While some estimations were made in this study, they were neither arbitrary nor generalized. In most cases, the values used to assess waiting time reduction, vehicle flow increase, CO\({}_{2}\) emissions reduction, and economic savings were directly obtained from the analyzed terminals. However, in certain instances where specific data were unavailable, estimations were made based on comparisons with similar terminals and established operational standards. The adopted methodology provides a coherent reference framework to evaluate the impact of automation and the Internet of Vehicles (IoV) in Ro-Ro terminals, considering both current scenarios and technology-optimized conditions. The reliability