## 4 Classification and Core Components of DT Systems in Transportation Infrastructure
### Core Components of DTs in Transportation
#### 4.2.1 Key Technologies and Features
Simulation models have a wide range of applications in lifecycle management. For instance, agent-based models can simulate the behavior of individual vehicles within a road network, enabling transport authorities to predict congestion patterns and test various traffic control strategies [48]. Additionally, simulation models can assess the impact of infrastructure changes, such as the construction of new roads or bridges, on traffic flow. They can also evaluate the effects of environmental conditions, such as weather changes, on transportation systems. Predictive models are critical for enabling digital twins (DTs) to forecast future scenarios, such as traffic flow volumes, infrastructure degradation, and maintenance or refueling requirements. These models primarily utilize machine learning algorithms--including neural networks, decision trees, and regression models--to identify patterns in historical data and generate actionable predictions. For example, a DT designed for bridge health monitoring integrates real-time sensor data (e.g., stress, vibration, and temperature measurements from strain gauges and accelerometers) with historical maintenance records to predict structural failures, facilitating proactive interventions [35]. The selection of data sources is guided by their capacity to capture long-term degradation trends and real-time operational conditions, which are critical for ensuring prediction accuracy. To ensure reliability, raw sensor data undergo preprocessing steps such as noise removal, normalization, and feature extraction to identify key indicators of structural health, including abnormal vibration frequencies. However, developing robust predictive models requires access to extensive high-quality datasets, significant computational resources for real-time processing, and ongoing calibration to adapt to dynamic environmental and operational conditions [50]. These challenges underscore the need for scalable data pipelines and adaptive algorithms to improve the efficacy of predictive models in DTs. By integrating data from multiple sources, simulation and predictive models provide a holistic perspective on the lifecycle of transportation systems. These models establish connections between different lifecycle phases, enabling seamless transitions from design to operation, maintenance, and optimization. For example, simulation results from the design phase can inform operational strategies, while predictive insights from the operation phase can guide maintenance and upgrade decisions. Three-dimensional modeling and visualization are pivotal in intuitively representing complex infrastructure systems throughout their entire lifecycle. Digital twins (DTs) leverage advanced technologies such as building information modeling (BIM), geographic information systems (GIS), and light detection and ranging (LiDAR) to create highly accurate 3D models of physical assets [30]. These models not only provide a visual representation of infrastructure but also integrate critical data on material properties, structural health, and environmental conditions, enabling comprehensive lifecycle management. From the design and construction phases to operation and maintenance, 3D visualization transforms these models into interactive tools. Urban planners, maintenance teams, and other stakeholders can navigate and interact with the DT in real-time, exploring its features and functionalities [38]. For instance, a DT of an urban transportation system can utilize 3D models to simulate traffic flow, allowing urban planners to identify bottlenecks and evaluate the potential impact of capital investment plans [60]. This capability facilitates remote inspections, enabling engineers to assess infrastructure conditions without being physically present, thereby saving time and resources. However, the application of 3D modeling in DTs is not without challenges. Generating precise and accurate 3D models demands high-resolution data from sources like LiDAR scans and satellite imagery, which can be both costly and technically demanding [61].