# A Digital Twin for Intelligent Transportation Systems in Interurban Scenarios
## 8 Conclusions and Future Work  
This paper presents the development and validation of an interurban digital twin designed to replicate a portion of the C-32 highway in Spain, with the objective of recommending traffic strategies to reduce congestion and pollution. This digital twin is intended to use real-time data from the highway and compare the current scenario with several strategies aimed at alleviating congestion or pollution episodes, depending on the situation. The system will select the best strategy and send it to the real world. This paper details the architecture of the digital twin, which uses a mobility simulator to model the real-world scenario and evaluates different strategies. The simulation outputs are used by the Decision Engine to identify the most effective strategy. We have conducted many tests to design the Decision Engine, including the selection of KPI's for decision-making and the adjustment of their weights for both the congestion and pollution use cases. Additionally, the work provides a brief discussion on the time needed to select a strategy and presents a validation using 100 real incident situations to evaluate the impact of each decision in each use case. Future work will focus first on incorporating behavioral variability into the simulations, particularly regarding the percentage of drivers who follow recommended strategies. By introducing probabilistic compliance models, the digital twin will better reflect real-world dynamics and enable more accurate performance assessments under varying adoption rates. This will require further data collection and collaboration with highway operators to obtain compliance metrics from actual deployments. Building upon this enhanced behavioral modeling, we also plan to improve the Decision Engine by incorporating machine learning techniques, specifically Reinforcement Learning (RL), to enable faster and more dynamic decision-making. The idea is to use the digital twin's simulation environment as a training ground for the RL agent, which will iteratively explore and learn the best traffic strategies across a variety of traffic conditions and incident types. Once trained, the RL model will replace the simulation-based loop during inference, allowing the system to select strategies in real-time without the need for repeated simulations. The learned policy will then be validated against previously unseen traffic scenarios to ensure generalization, using the KPIs defined in this paper. This approach promises to significantly reduce decision latency while maintaining or improving performance outcomes. As the penetration of V2X-enabled and autonomous vehicles increases, we intend to gradually transition to real-world validation of the system. While current evaluation relies on simulation due to limited connectivity on the C-32 highway, future deployment will enable on-site testing of complex, nonlinear effects such as delayed driver reactions, partial compliance, and sensor imperfections. We foresee an incremental rollout beginning with pilot deployments in areas of higher connected vehicle density, eventually expanding to full-scale validation of the digital twin under operational conditions. In parallel, as the system matures, we see potential for influencing transportation policy and ITS system design. By providing a data-driven framework for evaluating traffic strategies, the digital twin could support evidence-based policymaking and inform standardization efforts related to decision-making automation and V2X integration. E.L.-B. : conceptualization, formal analysis, investigation, methodology, software, validation, visualization, writing--original draft, writing--review and editing. E.C.-M.: conceptualization, formal analysis, investigation, methodology, software, validation, visualization, writing--original draft, writing--review and editing. C.V.-V.: conceptualization, formal analysis, investigation, methodology, software, validation, visualization, writing--original draft, writing--review and editing. E.L.-A.