## 6 Current Applications of DTs in Transportation Infrastructure
### Traffic Management  
Through the integration of technologies such as the IoT, big data, BIM, AI, and augmented reality, DTs enable devices to transmit data states from the bottom up and facilitate remote automatic control, transforming them from being \"blind, deaf, and mute\" into responsive entities.  
The second most important area in which the role of DTs is significantly enhancing is traffic management. Since large sections of road transportation can be built piece by piece with the help of GIS technology, DTs provide real-time data on integrations and simulation capabilities to optimize the flow of traffic without congestion and improve safety. Advanced traffic management systems using DTs capture data from a wide range of sources, including sensors in the road, traffic cameras, and GPS from vehicles. It then takes that information and monitors current conditions to make dynamic adjustments to traffic signals, lane usage, and speed limits. The DT model works as a data-driven simulation platform, eliciting information on, for instance, the impact of new technology deployments, such as intelligent signal control or perhaps traveler information systems, in a real-time environment. These systems monitor impacts and provide insights into how ITS technologies impact road traffic and environmental performance [106]. Related research highlights the use of model-based systems engineering (MBSE) technology systems and tools, designed based on DT technology, to help simplify the complexity of command operating systems and make them more manageable [107].  
This is being practically applied in London through Transport for London (TfL) DT, which is used for the monitoring of various traffic conditions and the management of the road network. The DT integrates data from thousands of sensors and cameras, enabling it to make real-time adjustments in traffic signals and route guidance for public transportation. Therefore, it significantly reduces congestion during rush hour traffic and offers quick responses in the case of road incidents. However, deploying such systems is not without challenges. Real-time traffic management involves processing huge amounts of data in the shortest time possible with high accuracy, leading to computationally intensive and data handling challenges. Further, reaping the benefits of data integration from heterogeneous sources, which come in a variety of formats and standards, involves sophisticated data processing techniques along with strong communication protocols [59].  
Another critical application of DTs in traffic management involves the application of DTs in predictive analytics. These DTs analyze historical data on traffic conditions against real-time input to predict future traffic conditions and, consequently, mount preventive measures against building congestion. For example, a DT inspects and analyzes the development of traffic congestion resulting from either an event scheduled to take place or weather conditions, thereby prompting authorities to take preemptive traffic control measures, such as the diversion of traffic or adjustment of traffic signal timings [32]. Thisproactive management of roadways improves not only the flow of traffic but also enhances the safety of the roads by minimizing the chances of accidents associated with congestion.  
While designing a simulation environment that exactly replicates on-road conditions can prove to be very expensive and ineffective, the online game GTA5 features a range of well-executed objects, pedestrians, and roads; it can always be used as a foundation for simulating self-driving cars. Through OpenCV's capture of the GTA5 game screen and image analysis using YOLO and Python-based TensorFlow to design algorithms for target collision avoidance and lane recognition, highly precise target recognition can be achieved [108].