Leveraging Digital Twin Technology for Sustainable and Efficient Public Transportation

Public transportation systems face numerous challenges like traffic congestion, inconsistent schedules, and variable passenger demand. These issues lead to delays, overcrowding, and reduced patron satisfaction. Digital twin (DT) technology is a promising innovation for improving public transportation systems by offering real-time virtual representations of physical systems. By integrating real-time data from various sources, digital twins can enable predictive analytics, optimize operations, and improve the overall performance of public transportation networks. This work explores the potential of digital twins to optimize operational efficiency, enhance passenger experiences, and support sustainable urban mobility. A comprehensive review of the existing literature was conducted by analyzing case studies, theoretical models, and practical implementations to assess the effectiveness of DTs in transit systems. While the benefits of DTs are significant, their successful implementation in bus transportation systems is impeded by several challenges like scalability limitations, interoperability issues, and technical complexities involving data integration and IT infrastructure. This paper discusses ways to overcome these challenges, which include using modular designs, microservices, blockchain for security, and standardized communication for better integration. It emphasizes the importance of collaboration in research and practice to effectively apply digital twin technology to public transit systems.