# A Digital Twin for Intelligent Transportation Systems in Interurban Scenarios
## 6 Performance Evaluation Results
### Results of Congestion Use Case Evaluation
#### 6.1.1 Variable Speed Limit--Incident Scenario  
Without incident  
This set of simulations was conducted to evaluate the impact of speed limit reduction strategies in the absence of incidents, focusing on the southbound direction of the highway. The baseline speed limit of 120 km/h was compared against reduced limits down to 50 km/h. Figure 5 shows the results across three scaling factors, all of which follow a similar trend. As the speed limit decreases, occupancy increases, since vehicles spend more time in the section and remain closer together. Conversely, intensity decreases, as lower speeds result in fewer vehicles passing per hour. The average vehicle speed drops from approximately 103 km/h under default conditions, with a speed deviation decreasing from 19 km/h to 15 km/h, which is considered low given the high-speed context. The Speed-Dev-Ratio (standard deviation over average speed) is maximized at the lowest speed limit (50 km/h), where deviation is lowest. As expected, total travel time increases as the speed limit is reduced. While the Speed-Dev-Ratio suggests 50 km/h may be optimal, other KPIs favor maintaining the default 120 km/h speed. Thus, a trade-off is required when selecting the most appropriate strategy. With incident  
This section includes the results when there is an incident. The nature of the incident presented was explained in Section 5. We performed the simulations in stages. First, the effect of reducing the speed limit to 50 km/h was tested. Then, from the best results of these simulations, the speed limit of the previous sections due to the incident on the highway was also reduced. Figure 5 shows the results of reducing the speed limit of the actual section and different scaling factors. In the occupancy graph, for factors 5 and 4.75, occupancy decreases slightly until the 80 km/h strategy, where it reaches a minimum before increasing again. Therefore, 80 km/h is the optimal strategy based on occupancy. Nevertheless, for factor 4.5, the minimum values occur at default, 110, 100, and 90 km/h. Therefore, the best choice for this factor would be not to apply any speed reduction strategy, as it does not improve the occupancy parameter. In the intensity graph, factors 5 and 4.75 also have a clear maximum for the 80 km/h strategy, making this strategy the chosen one. The same cannot be observed with factor 4.5, where the graph is constant until 80 km/h, and for lower speed limits, intensity starts to decrease. The decision, as in the occupancy, would be not to apply any strategy. The average vehicle speed graph shows lower values compared to when there is no incident, as the congestion in one section reduces the average speed of the entire highway. For factor 5, the average speed remains constant until the 80 km/h speed limit, where it starts to decrease. The speed deviation decreases consistently in this case. For factor 4.75, the average speed is nearly constant until the 80 km/h strategy, where there is a peak, after which it starts to decrease. Its speed deviation also follows a decreasing trend. For factor 4.5, there is a peak at the 110 km/h speed limit and decreases considerably after the 90 km/h limit. The speed deviation graph follows the same trend. The graphs corresponding to the Speed-Dev-Ratio show a similar trend for the three different scaling factors. The Speed-Dev-Ratio expresses the relationship between the average vehicle speed and the standard deviation of the speed between vehicles, reaching a maximum when there is a balance between the speed sacrificed, but the vehicles carry similar speeds. Factors 5 and 4.75 present their  
Figure 5: Simulation results with incident A for the variable speed limit strategy applied to the incident section. best performance for the 70 km/h speed limit, whereas for factor 4.5, it is for 70-60 km/h. As a result, if only this parameter is kept in mind, these strategies would be the chosen ones. Finally, the behavior of the total travel time changes according to the scaling factor.