## 4 Classification and Core Components of DT Systems in Transportation Infrastructure
### Classification of DT Systems in Transportation Infrastructure
#### 4.1.1 Functional Scope  
DTs in transportation infrastructure can be divided into three main categories based on their primary functions: Monitoring and Control, Simulation and Analysis, and Predictive Maintenance and Optimization. 1. Monitoring and Control This category deals with the gathering and visualization aspects of real-time data for monitoring infrastructure conditions and managing traffic flow. For example, a DT utilized in an urban traffic management center collects real-time data from IoT sensors, cameras, and GPS devices regarding current traffic conditions to dynamically adjust the timings of traffic signals [31]. Other environmental factors that may also be integrated into DTs for monitoring include weather conditions and public transit schedules, which contribute to a comprehensive view of traffic operations [30]. While these methods effectively aim to reduce congestion, challenges associated with data variety and latency arise during the implementation of such systems [32]. 2. Simulation and Analysis Simulation and analysis-centric DTs allow urban planners and engineers to model different transportation scenarios and analyze the impact of interventions. For example, the transportation department in a city may want to use a DT to simulate the effect of adding a new bus rapid transit (BRT) line on existing road traffic. By taking into account various factors such as passenger demand, road capacity, and traffic signal timings, a DT can provide approximate pre-information on congestion and enable optimizations of route design [33]. However, this requires substantial computational resources to generate simulation models with high-quality input data [36]. 3. Predictive Maintenance and Optimization Predictive maintenance digital twins (DTs) utilize real-time sensor data and historical trends to evaluate infrastructure asset conditions and forecast maintenance requirements. A notable application involves DTs in bridge health monitoring, where embedded sensors collect stress, vibration, and temperature data. Machine learning algorithms process and analyze these data streams to predict structural failures, facilitating proactive maintenance interventions [13]. Real-time sensor data in such systems are typically derived from strategically positioned sensors (e.g., strain gauges, accelerometers, and temperature sensors), which deliver continuous, high-resolution insights into structural integrity. Raw sensor data undergo cleaning and normalization to ensure suitability, followed by feature extraction to identify critical indicators of deterioration. Machine learning models, such as regression \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Classification** & & **Key Features** & **Example** & **Key Challenges** & **Refs** \\ \hline \multirow{4}{*}{
\begin{tabular}{c} By Data \\ Integration \\ \end{tabular} } & \multirow{4}{*}{Static Data-Based DTs} & - & Relies on design specs, geographical info, and historical data & Evaluating new road layouts and materials based on historical usage [51] & Limited adaptability to real-time changes; slower to reflect realities [52] & [51; 52] \\  & & & Planning and design & \\ \cline{1-1}  & & & Continuously updated from IoT sensors, GPS, weather data, etc. & City-wide sensor-driven traffic control and congestion alerts [53] & Ensuring data consistency and low latency at large scales [54] & [53; 54; 55] \\ \cline{1-1}  & & & Combines static (blueprints) and dynamic (sensor) data & Railway DT integrating design data with real-time loads [3] & Complex system integration; requires standard data formats [53] & [3; 53] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._algorithms and neural networks, then analyze the processed data to generate predictive outputs. However, predictive maintenance DTs encounter challenges, including data integration, model accuracy, and the computational intensity of real-time large-scale dataset processing [5; 41].