# A Digital Twin for Intelligent Transportation Systems in Interurban Scenarios
## 2 State of the Art on Digital Twins for Intelligent Transportation Systems
### Other Prototypes  
Other studies that do not fall into either of the previous categories have also been designed. In [23], a prototype for a real-time digital twin of the traffic environment is presented. The implemented system provides, as the main service, the visualization of the digital twin with a 3D simulation, so the focus is on data acquisition and digital twin building. There is no decision-making or simulation. The methodology used is based on a proposed architecture, which includes a cloud server and wireless sensors connected to it, called the Central Perception System Architecture. This communication is over 5G or DSRC using SENSORIS [24] message format. Unity is used for visualization. A simple field implementation has been carried out with a central server in the cloud and three sensors.  
Similarly, the work in [25] identifies the steps needed to generate a highly accurate digital twin. Three different test fields are implemented: urban, rural, and highway. Data is acquired and forwarded in real time through 4G and ITS-G5 to a central computing server to update the digital twin. Data management is performed with the open-source Apache NiFi project. Only the data acquisition and updating of the digital twin are presented.  
In [26], the authors propose a framework for a Mobility Digital Twin (MDT). The physical space contains sensors, cameras, and LiDARs, and their samples are sent to the digital space. The digital space is in charge of storage, modelling, learning, simulation, and prediction. Machine learning is applied to learn performance and preference for vehicles. A cloud-edge architecture is used to enable faster updates. An example of a cloud-edge architecture is implemented with Amazon Web Services (AWS), and a case study on personalized adaptive cruise control is performed.  
Finally, the work in [27] tries to solve two problems in implementing digital twins. The first is that in a real transportation environment, vehicles have different levels of automation (vehicular heterogeneity), and the second is that a digital twin requires large quantities of computation, which implies high delay. To tackle both problems, a digital twin-empowered edge AI framework is proposed. This framework consists of three domains: vehicle, edge AI, and digital twin domains. Real-time responsiveness is guaranteed in the edge AI domain. The digital twin domain performs more advanced algorithms, such as deep reinforcement learning, to improve traffic efficiency. SUMO, with OMNeT++, is used for simulating the decisions made by intelligent algorithms. The paper does not present a field implementation.  
To conclude, there are many studies in the literature in the area of transportation digital twins. Some of these studies focus on creating a digital replica to monitor and understand the real world but do not complete the loop from the digital world back to the real world, while others emphasize decision-making processes that are injected back into the real world. Notwithstanding, these studies bring us a step closer towards understanding the feasibility and intricacy of building a real-time digital twin that incorporates decision-making software. However, it can be noticed that most of the research has concentrated on the study of microscopic use cases, which are addressed to individual vehicles. Nonetheless, there exists a research gap in the exploration of macroscopic-level control decisions aimed at coordinating and optimizing highway systems, which our study tries to address.