# Leveraging Digital Twin Technology for Sustainable and Efficient Public Transportation
## 5 Discussion  
Digital twin technology has been widely used in industries like manufacturing, maritime operations, and healthcare, but its potential in public transportation remains under-explored in urban and non-urban settings. The integration of DT technology into public bus transport systems presents significant impacts by improving operational efficiency and operations and reducing delays. As demonstrated by several studies, DTs can use real-time data analysis and predictive maintenance to predict traffic patterns, allowing for better planning of bus routes and schedules. For instance, a study in Brazil demonstrated a DT's ability to improve the traffic flow and reduce delays at intersections by simulating and predicting traffic situations [102]. Additionally, the implementation of DTs can assist in smarter decision-making to improve efficiency and scalability based on real-time transit demand [46]. Moreover, predictive maintenance also enables the early identification of issues within bus systems, ensuring a longer lifespan and reduced operational costs [34; 62; 100].  
While the applications of DTs in public transit systems are considerable, there are certain gaps in the research that require attention. One of the identified gaps is that research has focused on short-term impacts without considering the long-term sustainability of these improvements. There were only a few studies that discussed how the benefits of DTs continue to scale over time. Additionally, there is a lack of research integrating diverse data sources such as passenger counts, vehicle health data, and environmental factors like weather into a single framework. The integrated system could be more beneficial in providing a more comprehensive approach to decision-making [49; 102].  
There is also a lack of studies that incorporate the scalability and adaptability of DTs across multiple geographical locations. While several studies focus on the successful implementation of DTs in well-developed urban areas, there is lack of research in smaller towns or rural areas. The latter places could have different traffic patterns and problems, less reliable infrastructure, or fewer resources [49]. Hence, it is important to understand how DTs could be adapted for non-urban areas [34].  
Another concern is that there are only a few studies that address the risks and mitigation strategies to handle real-time and sensitive data. More research is required to strengthen cybersecurity measures and alleviate data privacy concerns that have been mentioned in different studies [35; 63]. There is also a lack of comprehensive long-term economic feasibility of implementing DT technologies in bus transit systems. Although DTs improve bus operations, there are not many studies that have analyzed the costs involved. Transit authorities need to understand the potential economic benefits and trade-offs that will be essential to consider the adoption of DT technologies. Another crucial gap is the social equity implications of DTs in public transportation. There is not much research showing how DTs can help reduce transportation disparities, especially for underserved communities and regions with limited access to public transit services [34; 49].  
While DT technology shows multiple benefits for bus transit systems, it is important to address the gaps to standardize adoption and unlock its full potential. More research is required to understand the long-term sustainability of DTs' performance. There is also research required to integrate multiple data sources in order to determine the economic and social equity implications and feasibility in different geographic locations. Future studies should also explore standard ways to protect data privacy and cybersecurity without losing the benefits of real-time analysis. When these gaps are addressed, DTs can play an even more significant role in public transportation system operations, benefiting both passengers and operators.