## 4 Classification and Core Components of DT Systems in Transportation Infrastructure
### Core Components of DTs in Transportation
#### 4.2.1 Key Technologies and Features  
1. Data Integration and Real-Time Interaction  
Data integration provides the backbone for DTs in transportation, merging the physical and virtual worlds. A DT integrates manifold data, which originate from sensors embedded in infrastructure (such as bridges and roads), traffic cameras, GPS devices, and environmental monitoring stations [56]. These different feeds contribute to a clear, real-world perspective on transportation, enabling a holistic lifecycle management approach. Data integration not only serves as the foundation of DTs but also plays a critical role in the lifecycle management of transportation systems. During the design phase, DTs leverage historical and environmental data to optimize system layout and functionality; during the operation phase, real-time data integration provides dynamic updates, enabling real-time monitoring and operational efficiency; during the maintenance phase, data integration supports predictive maintenance, reducing unexpected failures and repair costs; and during the optimization and upgrade phase, data integration offers comprehensive support for decision-making, helping to evaluate system performance and plan future improvements. Real-time interaction is the two-way flow of data between a physical system and its DT. This ebb and flow allows real-world modifications to be mirrored instantaneously in the DT, which aids in optimizing physical operations through digital insights [57]. For example, a DT with up-to-the-minute data about vehicle flow, road conditions, and weather could automatically adjust traffic signal times and lane usage in traffic light control [58]. Real-time interaction not only enhances operational efficiency but also has a profound impact on other lifecycle phases. During the design phase, real-time interaction enables virtual simulations to test and optimize design solutions; during the operation phase, it allows DTs to dynamically adjust system parameters in response to emergencies; during the maintenance phase, it supports remote monitoring and maintenance, reducing costs and downtime; and during the optimization and upgrade phase, it provides real-time feedback to ensure the effectiveness of improvement measures. However, one of the significant challenges of data integration is ensuring that this information is accurate and delivered in a timely manner. Wrong or outdated information may lead to incorrect decisions within the transportation system [59], potentially compromising the effectiveness of lifecycle management. For example, inaccurate sensor data may lead to incorrect assessments of bridge structural health, while delayed data may prevent DTs from effectively optimizing traffic flow. Additionally, data consistency issues increase integration complexity, potentially affecting the overall performance of DTs. Advanced techniques, such as machine learning algorithms, are being researched to process real-time data streams for noise filtering and error detection [5]. Edge computing technology can also reduce data transmission delays by distributing data processing tasks to the network edge, thereby improving the efficiency of real-time interaction. Despite these efforts, seamless real-time data integration remains an area needing improvement, as it is critical to achieving the overarching goal of holistic lifecycle management. Simulation and Prediction Models  
Simulation and predictive models lie at the heart of how DTs work, enabling the analysis of numerous scenarios and the forecasting of future system states. These models play a critical role in lifecycle management by supporting decision-making across all phases. During the design phase, simulation models allow designers to test and optimize system layouts; during the operation phase, predictive models enable real-time monitoring and optimization; during the maintenance phase, they facilitate predictive maintenance by identifying potential failures; and during the optimization and upgrade phase, they provide data-driven insights for system improvements.