# Leveraging Digital Twin Technology for Sustainable and Efficient Public Transportation
## 4 Case Studies and Applications  
Case studies for DT technological application were searched and filtered to incorporate diversity in technological and geographical aspects in addition to real-world applications. This was to ensure the inclusion of different operational aspects of bus transport systems, such as traffic flow management, maintenance optimization, and route planning from different regions. The implementation of digital twin (DT) technology in bus transport systems has demonstrated measurable improvements in various operational aspects, including reduced delays, enhanced maintenance cycles, and increased passenger satisfaction. These improvements result from DT's ability to provide real-time data analysis and predictive maintenance capabilities [53]. Reduction in delays is one of the main benefits of DTs when it comes to bus transport. By utilizing real-time data from sensors and IoT devices, digital twins can simulate and predict traffic patterns, enabling better route planning and scheduling. For instance, the authors of one study highlight the role of DTs in ITSs, which can facilitate real-time monitoring and management of traffic flows to minimize delays at intersections and along bus routes [110]. The authors implemented a DT at a ramp merge, non-signalized intersection in Brazil with the aim of building models and applications, which will allow visualize scenarios to analyze accident situations and avoid them. Additionally, the systematic mapping study mentions that DTs can diagnose and address issues in transportation systems, while suggesting that most existing approaches are limited to system monitoring or fault detection rather than comprehensive diagnostic reasoning [102]. By utilizing real-time data analytics and simulation capabilities, DTs can predict and mitigate potential delays caused by traffic congestion, which can be very helpful in bus transit systems as well. As detailed in Section 2.3 of this paper, the case study conducted in Badalona, Spain demonstrates how a DT enhances bus operations by improving transit efficiency and safety. The authors were able to utilize a genetic algorithm to model traffic and schedules, which adapts to anomalies, enabling real-time system monitoring [87]. In another study that evaluated a day-long simulation in Geneva, the model was able to accurately replicate the real traffic (where traffic counters were present) using SUMO [113]. In another study, by incorporating schedule recovery on a simulated bus route in Guangzhou, China, the authors were able to demonstrate a reduction in waiting times, as well as bus travel times. This indicates that integrating DT frameworks with traffic data can enhance the management of bus schedules if implemented properly by authorities to optimize routes and minimize wait times for passengers [49]. Improvement of the passenger experience was also discussed in Section 2.3, where a study compared two models, with and without a DT, in Anhui Province, China [91]. The direct benefits of incorporating a DT into the bus transit process model provided more seating availability for passengers to address supply and demand in transit. Furthermore, the application of such technologies allows for dynamic adjustments to bus frequencies based on real-time demand, which has been shown to improve efficiency, effectiveness, and scalability [111]. Digital twins also enhance the efficiency of maintenance operations by utilizing predictive analytics for maintenance requirements based on real-time performance and usage data. This proactive approach can help prevent unexpected failures, thus extending the lifespan of bus components and reducing operational costs. Studies have highlighted DTs' ability to optimize maintenance planning and minimize downtime by utilizing insights derived from comprehensive data analysis from various health monitoring sensors in buses [34; 62; 100] Furthermore, as mentioned in Section 2.3, passenger engagement was also explored in user-centered digital twin models in Gujarat, India [23].