# A Digital Twin for Intelligent Transportation Systems in Interurban Scenarios
## 7 Digital Twin Validation  
A validation of the digital twin has been conducted to ensure the reliability of the Decision Engine and to confirm that the selected strategies improve the incident situation, reducing the congestion or pollution levels. This evaluation is performed using the final implementation of the digital twin, which processes real-time data collected by the Aggregation module and alerts from the Pre-processing module. Consequently, the Decision Engine and Traffic Simulator modules operate on current sensor data. Moreover, the scenario utilized is expanded to replicate the entire southern sector of the C-32 highway, covering approximately 56 km. Importantly, the recommended strategies are not only generated in real time but also communicated back to the physical infrastructure using V2I communications via IVIM messages, and to end users through a mobile application developed by the road operator via the cellular communication network. In this validation, variable speed limit strategies have been considered for both congestion and pollution use cases.  
To evaluate both use cases, 100 triggers collected over eight months on the highway were tested, and a decision on the variable speed limit to apply is recommended. The KPI results from the default and selected strategy simulations are compared in cases where the decision differs from the default to evaluate the improvement of the decision made in each trigger situation. In Tables 5 and 6, for the decision parameters used in each use case, the average improvement across all decisions and the maximum improvement values are presented, expressed as percentages.  
For the trigger executions using the congestion Decision Engine, as shown in Table 5, all parameters have improved on average, particularly the Speed-Dev-Ratio, which experienced an average increase of 0.93%. In the case of travel time, the average improvement is negative, as a reduction in this parameter is considered beneficial. A noteworthy aspect is the maximum improvement percentage for each parameter, where travel time showed  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Time** & **CO** & **NO\({}_{\mathbf{x}}\)** & **PM\({}_{\mathbf{2.5}}\)** \\ \hline Average improvement (\%) & 5.23\% & \(-4.56\%\) & \(-1.66\%\) & \(-2.28\%\) \\ Maximum improvement (\%) & 0.15\% & \(-8.3\%\) & \(-3.78\%\) & \(-5.51\%\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Pollution validation results.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Time** & **Intensity** & **Speed-Dev-Ratio** \\ \hline Average improvement (\%) & \(-0.24\%\) & \(0.12\%\) & \(0.93\%\) \\ Maximum improvement (\%) & \(-5.17\%\) & \(2.71\%\) & \(2.67\%\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Congestion validation results.  
a 5.17% reduction, intensity increased by 2.71%, and the Speed-Dev-Ratio presented a 2.67% rise.  
When executing the triggers for the pollution use case, all pollutants show an average reduction, especially CO, with an average decrease of 4.56%. Moreover, the emission parameters showed a maximum reduction of 8.3% for CO, 3.78% for NO\({}_{\rm x}\), and 5.51% for PM\({}_{\rm 2.5}\). However, these improvements are achieved by reducing the average vehicle speed, which results in a 5.23% increase in travel time on average, with a minimum increase of 0.15%. This increase in travel time is not critical, as the primary focus of the use case is on reducing emission levels, which has been achieved.