# A Digital Twin for Intelligent Transportation Systems in Interurban Scenarios
## 1 Introduction  
A Digital Twin (DT) has been defined as \"the creation of virtual models for physical objects in a digital way to simulate their behaviors\" [1]. Essentially, a DT is a digital replica that models reality, thus providing a clear picture of the physical system being analyzed, recording its history, and serving as a powerful platform for decision-making and experimentation. Digital twins can be used for a wide range of applications such as manufacturing, aviation, healthcare, communication networks, Intelligent Transportation Systems (ITS), and urban intelligence, among others, and are characterized by three components: the physical world or object, its digital (virtual) counterpart, and the communication channel between them (cf. Figure 1). A bidirectional communication between both physical and digital worlds is a key aspect of a digital twin, as the physical world must feedinformation to the virtual world so that it is able to update its state in real-time, to perform tests or training, and to send the results back to the physical world. This paper presents the development, analysis, and validation of a transportation digital twin, where transportation modelling or transportation digital twin is one of the functions of ITS. A transportation digital twin keeps a digital copy of a road, highway, village, or city in real-time and has numerous applications. Notably, these include digital twins used for simulating autonomous vehicles in real-world conditions [2], monitoring and predicting future traffic situations [3], road planning [4], and optimizing traffic and road safety [5]. However, it is important to note that the majority of the digital twin applications are used to address microscopic aspects, which involve the application between individual vehicles. There is relatively little research on macroscopic traffic control, which involves managing large-scale transportation systems, including overall traffic flow and congestion patterns [6]. A more detailed review and classification of transportation digital twins is provided in Section 2, focused on the state of the art. A transportation digital twin requires real-time data to accurately reflect real-world conditions. This data is provided by sensors located along the road or through Vehicle-to-Everything (V2X) communications from connected vehicles. Connected vehicles send periodic Cooperative Awareness Messages (CAMs) to inform precisely of their position, whereas information from sensors can provide the number of vehicles passing through a specific point of the highway. Whether using V2X communications, sensors, or both, the digital twin uses this information to make decisions. These decisions are then transmitted back to the physical system using Vehicle-to-Infrastructure (V2I) communication via Infrastructure to Vehicle Information Messages (IVIM), and are also communicated to drivers through a mobile application developed by the road operator, which uses the cellular communication network. This dual-channel communication closes the loop between the physical and virtual worlds, enabling real-time feedback and action. To the best of our knowledge, there is no other study in the literature that develops a complete interurban digital twin that uses real highway data to make decisions to improve traffic congestion efficiency and minimize environmental impact in a macroscopic manner. Motivated by this context, in this work, we develop a transportation digital twin that replicates a segment of the C-32 highway in Barcelona (Spain), with the objective of mitigating traffic congestion and reducing pollution emissions by applying specific traffic strategies such as changing the speed limit and recommending vehicle re-routing on the highway. Specifically, the work presented in this paper is focused on the following:  
Figure 1: Components of a digital twin. Development of a transportation digital twin to reduce traffic congestion and pollution emissions. * Proposal and validation of traffic strategies applied by the digital twin for improving performance.