## 4 Classification and Core Components of DT Systems in Transportation Infrastructure
### Basic Framework of DTs of Urban Transportation Infrastructure
#### 4.3.1 Four Horizontal Layers  
1. Data Layer  
The data layer serves as the foundational layer throughout the lifecycle of urban transportation infrastructure. It is responsible for collecting, integrating, and processing both dynamic and static data from physical entities. This layer evolves as the infrastructure progresses through the following lifecycle stages:  
* Design and Construction Phase: During this phase, the data layer primarily focuses on structural data, such as the geometric and material properties of bridges, roads, and tunnels, which are critical for ensuring that the infrastructure is built to specifications. Environmental data, such as soil conditions and weather patterns, is also collected to inform design decisions. * Operation Phase: In this phase, the data layer shifts its emphasis to operational data (e.g., traffic flow, speed, and congestion) and environmental data (e.g., temperature, humidity, and pollution levels) captured by sensors. These datasets provide real-time insights into the performance of the infrastructure and its interaction with the environment. * Maintenance and Decommissioning Phase: Historical data become crucial here, as they enable trend analysis and predictive maintenance. For example, historical traffic and incident records help identify patterns that inform maintenance schedules. Behavioral data, which record the interaction patterns of traffic participants, are also vital for optimizing traffic management and ensuring safety. Throughout the lifecycle, the data layer ensures that the virtual models in the DT system are continuously updated with accurate and relevant data, which enables precise predictions, simulations, and decision-making. 2. Model Layer  
In the digital twin's model layer, complex virtual models of transportation infrastructure are created and maintained. These models are not just 3D replicas of physical entities but dynamic models that integrate detailed behaviors and interactions to simulate and predict how the transportation system will operate. These model layers take inputs from the data layer, and the data-driven model-based models provide decision-makers with insights through real-time simulation and behavioral predictions to help them understand and anticipate challenges that the transportation infrastructure may face. As the full infrastructure lifecycle progresses, the digital twin's model layers play a critical role at different stages: in the planning and design phase, simulation analysis helps assess the feasibility of different design options and predict traffic bottlenecks or structural weaknesses; in the  
Figure 3: “4 horizontal + 4 vertical + N” DT framework for urban transportation infrastructure. construction phase, the real-time monitoring of construction progress and quality ensures that the construction complies with design specifications; Confirm, the original text can be modified as follows Confirm, the original text can be modified as follows. The real-time updating and adjustment mechanism of the model layer makes it possible not only to command and optimize traffic flow through the control model but also to intelligently manage the traffic system in a changing environment, ensuring the optimal management of the infrastructure under changing traffic loads and external environments. This dynamic updating mechanism makes the digital twin not only a static monitoring tool but also an intelligent system with predictive capability and adaptive optimization to support the sustainable development of transportation infrastructure and provide decision-makers with full lifecycle management support. 3. Service Layer  
The service layer is a critical transformation layer in the digital twin, ensuring that outputs from the model layer can be effectively applied to the application layer. It provides key services and interfaces for translating complex data analysis and model predictions into actionable operations and decision support.