# Leveraging Digital Twin Technology for Sustainable and Efficient Public Transportation
## 3 Challenges and Limitations of Digital Twins
### Technical Complexities  
Integration of various data sources and technologies is one of the primary technical challenges in implementing DTs. Since DTs rely on real-time data from sensors, IoT devices, to create reliable virtual representations of physical assets, successful integration requires a complex IT infrastructure that can handle large volumes of data. This should be achieved to ensure seamless communication between physical and digital systems [100]. Management and data are also integral components of DTs. Effective data analysis is important for optimizing bus operations and improving service delivery. Some studies indicate that many organizations face problems in data management, including data silos, inconsistent data formats, and a lack of standardized methodologies for data integration [98]. The reliability and effectiveness of transportation systems are compromised when issues like these arise. The setting up and maintenance of the necessary IT infrastructure is also a challenge. The implementation of DTs can be a long and costly undertaking for many stakeholders since high-performance IT infrastructure is required for their operation [75]. Hardware and software components along with regular maintenance and updates are required to ensure DTs can represent the physical systems accurately as well as have continuous data synchronization. There are high associated cost and resource constraints when high-performance computing, cloud storage, and real-time data-processing capabilities are required. Multiple hardware and software requirements to collect and transit data efficiently are also of utmost importance. Continuous complex data synchronization to allow real-time decision-making while avoiding latency issues is also crucial. The lack or inefficiency of the infrastructure can lead to poor data accuracy, increased maintenance costs, system downtimes, and overall operational inefficiencies in bus transportation systems [26]. This can potentially lead the DT framework to be unreliable and incorrectly predict congestion patterns, inefficient bus routing, and increased passenger wait times. Technologies using DTs have different requirements for what level of detail is required to develop an accurate model of real-world conditions. Simulated environments used for training AVs require a high level of detail, like road surface simulation, realistic roads, realistic traffic flow, and sensor and accident simulation, which are necessary to properly train AV decision-making in a virtual environment [78]. The quality of the simulated data or environment has a direct impact on the decision-making of the AI system. Additionally, virtual environments may not be useful for training classification models operating on direct sensor data because of the small differences in simulations compared to the real world [27]. LOD choice plays a vital role in balancing accuracy, cost, and computational efficiency in DT applications. Higher LOD requirements generally lead to increased computational complexity, higher costs, and greater data storage and processing demands. However, they also provide accurate and reliable datasets that improve model performance. Conversely, lower LOD models are more cost-effective, easier to implement, and require less data. These factors are beneficial for applications where real-time processing, privacy, and security are priorities. For instance, a low LOD DT used for high-level traffic flow analysis may only require total vehicle counts and speed data, whereas a high LOD DT for AV training must incorporate intricate details (granular) such as lane markings, pedestrian behavior, and environmental conditions [27]. The cross application of DT technologies will apply unique issues. AVs and fleet and traffic management have unique tradeoffs, which enable the technologies to work in real time while being performant. Modern vehicles are trending towards predictive maintenance and already have a lot of stored operational data [45].