# 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
Factors 5 and 4.75 decrease the travel time as the speed limit decreases, reaching a minimum at the 80 km/h limit, and then increasing considerably. Factor 4.5 keeps a constant time until the 90 km/h strategy, where it starts increasing. The best traffic strategy is the one that minimizes travel time. As a result, in the first two cases, the best strategy would be the 80 km/h speed limit, while for factor 4.5, the default strategy would be chosen. The optimal strategy is determined by considering intensity, Speed-Dev-Ratio, and total travel time, as outlined in Section 4.2. Since occupancy correlates with intensity, and speed and deviation are captured by Speed-Dev-Ratio, the selected strategy is the one that maximizes intensity and Speed-Dev-Ratio while minimizing total travel time. For factor 5, the best strategy is 80 or 70 km/h, for factor 4.75, it is 80 km/h, and for factor 4.5, 90 or 80 km/h. Another strategy that has been tested also involves changing the speed limit of the sections that preceded the incident. We identified the best results when modifying the speed limits of the section with the incident to 70, 80, and 90 km/h. Consequently, we changed the speed limits of the incident section and the previous ones (up to three sections) to 70, 80, and 90 km/h. Figure 6 shows the results. Observing the occupancy graphs, this parameter increases when reducing the speed limit of a higher number of previous sections. For factor 5, the minimum values are obtained when modifying zero, one, or two previous sections to 80 km/h. For factor 4.75, they are obtained only for zero or one previous section of the same speed limit. Differently, factor 4.5 obtains the best results at default or by changing zero or one section to 90 km/h. Inversely to the occupancy, the intensity obtains the best performance when reaching a maximum value. The intensity decreases when changing the speed limit of a higher number of previous sections. This is directly related to the fact that, as we are reducing the speed of previous sections, there are fewer vehicles per hour reaching the incident section, decreasing the overall intensity. Graphs for factors 5 and 4.75 show almost the same performance, maximizing their intensity for the speed limit of 80 km/h and zero or one previous section. Factor 4.5 follows a different pattern, where the highest intensity is achieved by reducing the speed limit of only the incident section or also of one previous section to 90 or 80 km/h. The speed graphs show that as more upstream sections have reduced speed limits, the average vehicle speed decreases overall. This is because lower upstream limits slow vehicles earlier, reducing the overall average speed. The speed deviation follows the same pattern as the average vehicle speed. In the Speed-Dev-Ratio graph, all factors follow a similar trend, where the maximum values are reached when modifying the speed limit to 70 km/h. For factors 5 and 4.75, the maximum value occurs when modifying two previous sections, and for factor 4.5, three previous sections. This fact can be attributed to the lower speed deviation in these strategies. The best travel time occurs when its values are minimum. For factors 5 and 4.75, it occurs when changing the speed limit to 80 km/h for zero or one previous section. For factor 4.5, without any strategy or changing the speed limit to 90 km/h for zero or one previous section, the best strategy would be not to apply any, as it does not improve the behavior. Having in mind all the previous parameters, the chosen strategies would be  
- Factor 5: 80 km/h for the incident section and two upstream sections (80_2)  
- Factor 4.75: 80 km/h for the incident section and one upstream section (80_1)  
- Factor 4.5: 90 km/h for the incident section and one upstream section (90_1)