# Leveraging Digital Twin Technology for Sustainable and Efficient Public Transportation
## 2 Review of Different Aspects of Digital Twins
### Current Applications of Digital Twins in Public Bus Transportation
#### 2.3.2 Traffic and Route Optimization  
Traffic congestion, especially on busy roads, can be greatly alleviated by optimizing bus routes. Furthermore, operators can prioritize efficient use of resources via data-driven decision-making that can not only improve passenger experience but also reduce emissions, leading to environmental benefits. One study to support optimized bus scheduling and passenger flow management discusses efficiently managing the number of vehicles to meet passenger demand while minimizing wait times for passengers. For instance, the algorithm proposed by the authors saw a reduction in the number of drivers required, leading to cost cutting [51]. Another study indicates that planners can make informed decisions when traffic optimization simulation models can predict the relationship between changes in bus routes and overall traffic patterns and congestion levels [90]. Improving traffic flow can also be achieved by optimizing traffic signal operations in urban areas. The authors of a study used decentralized graph-based multi-agent reinforcement learning incorporating DTs for traffic signal optimization. An approximately 55.38% reduction in fuel consumption over a 24-h scenario was achieved. The authors highlighted alleviating traffic congestion, increasing transportation efficiency, and enhancing the overall commuter experience as benefits in a larger-scale real-world application [68].  
Research regarding Vehicular Ad Hoc Networks (VANETs) discusses challenges such as VANETs' frequent network disconnections, irregular connectivity, lack of node management, and seamless communication/collaboration between different vehicles, which limit proper traffic optimization. To overcome this, the authors incorporate DTs into the networks, and that resulted in an increased energy efficiency, network lifespan, and data transmission. They also highlighted that the combined model was able to analyze network behavior and predict traffic patterns [65]. Urban traffic signal control using VANETs to improve traffic management has also been performed using multimodal traffic simulation software SUMO. The authors used a real-time vehicle fuel consumption index while modeling the DT via an optimization algorithm, iterative optimization, and green wave control simulations (coordinating phase differences between signal lights of the major road) [52].  
The concept of DTs was also found to be useful for Demand-Responsive Transit (DRT) to better simulate the relationship of traveler trip chain with the bus operation chain. The authors were successfully able to describe and validate the bus transit process between two stations in Anhui Province, China by using Automatic Vehicle Identification data. It was found that although the two models (one with and one without DTs) took largely the same time between two stations, passengers in the model with DTs had more seats available, thus enhancing the travel process by adjusting real-time supply and demand [91]. Like DRT, a dynamic bus-routing approach has also been studied to prioritize flexible transportation systems and passenger convenience. Using AI, the authors also optimize the average travel time by finding a globally optimal set of paths for a bus fleet [53].