## 3 Conceptual Overview of DTs in Transportation Infrastructure  
### Definition and Core Concepts  
The concept of digital twin (DT) was first introduced by [PERSON] [4]. A digital twin is a special type of physical entity or process that is realized through data linkages, ensuring a certain degree of consistency between the physical and virtual states.  
It provides a comprehensive perspective on the entire lifecycle of the entity or process, thereby aiding in overall performance optimization. A digital twin uses digital means to create a virtual representation of a physical entity, leveraging algorithmic models, historical data, and real-time data to simulate, predict, validate, and control the entity throughout its lifecycle [33].  
In the conceptual model proposed by [PERSON] [4], the digital twin comprises three core components: the physical product entity in the real world, the virtual product in the virtual space, and the data and information links that connect the virtual and physical products. In the context of transportation infrastructure research and applications, this study defines the core elements as follows: the physical transportation infrastructure in the real world, the virtual model in the information space, and the communication link connecting the two. \"Physical transportation infrastructure in the real world\" refers to existing or planned infrastructure such as roads, bridges, tunnels, and traffic signal equipment. \"Virtual model in the information space\" refers to the computer-generated model of transportation infrastructure in the information space, i.e., the digital twin, which not only reflects the appearance, dimensions, and textures of the physical infrastructure but also demonstrates its functions, operational status, and behaviors. The \"Communication link connecting the two\" serves as the bridge enabling data communication and interaction between the physical infrastructure in the real world and the virtual model in the information space, facilitating real-time data synchronization and information exchange.  
In transportation, DTs result in a virtual model, an exact replica of critical physical infrastructure assets. The virtual model is continuously updated with real-time data flows. Consequently, it reflects the current condition of the physical asset at any given moment and can even estimate future conditions [43]. Unlike static models, DTs are dynamic. They evolve continuously as new data from sensors, IoT devices, and weather monitoring systems are integrated [44]. This dynamic nature reveals the condition of the asset at any instant and supports predictive modeling and decision-making related to infrastructure [40].  
Furthermore, DT in transportation infrastructure exhibits two core characteristics. The first is lifecycle integration, which means that it supports every phase of infrastructure management, from initial planning to construction, operation, and maintenance. The second equally vital characteristic is real-time interaction. By connecting DTs with sensors embedded in physical assets, real-time reflections of infrastructure conditions are achieved [45]. This capability enables early warnings and proactive measures beyond mere monitoring. The DT technology stack includes machine learning algorithms, AI, cloud computing, and 3D modeling [39]. Advanced data analytics enable DTs to process vast amounts of information from diverse sources, creating opportunities for real-time decision-making and predictive maintenance.