# 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
#### 2.3.4 Safety and Emergency Response  
The concept of creating a virtual replica of a physical urban area and integrating data from multiple sensors and IoT devices in real time allows for monitoring the dynamic traffic conditions. Technologies such as sensors, 5G, and Wi-Fi enable data to be captured from physical spaces and shared with other vehicles (V2V), the cloud (V2C), pedestrians (V2P), as well as infrastructure (V2I). The high automation and intelligent control that can be achieved from planning to maintenance can encompass various transportation modes, including buses [38; 71]. Thus, DTs can enhance real-time traffic management by simulating multiple scenarios which can help in collision prevention and predictive analysis. DTs support emergency management by offering a comprehensive visualization of urban ecosystems. This visualization can help authorities understand the spatial dynamics of emergencies, enabling better planning and response, ultimately enhancing real-life emergency preparedness [55]. The authors of one study highlight that real-time traffic signal adjustments and routing can minimize collision risks, assess potential impacts, and develop effective response strategies. Further, dynamic response planning can be effective in events like natural disasters, where evacuation routes and emergency responses to protect civil infrastructures are prioritized [24; 56]. The concept of a transportation digital twin (TDT) system can also improve the safety and mobility of existing urban systems while reducing emissions. Not only can the route of emergency vehicles be optimized by optimizing physical traffic signals, but the systems can also be used to close and restrict certain routes in case of potential hazards, facilitating urban transportation management [72]. Although not mentioned explicitly, implementing real-time preventive measures and route optimizing can be very beneficial to avoid high-risk areas or times for public transportation vehicles [94]. Research was conducted on risk evaluation for a highway traffic management framework by integrating high-riskroadway identification, risk judgment, and control. Since buses are a significant component of highway traffic, the neural network for road segment risk identification could be applied to assess and manage risks associated with transit operations, ensuring safer and more efficient bus transit on highways [39]. Ensuring the safety and efficiency of bus operations is critical for maintaining public trust and encouraging the use of public transport. A study focused on vehicle stability analysis, trajectory deviation analysis, and risk assessment of vehicles driving along an expressway. The authors then created a DT to monitor various indicators like speed, sideslip, and rollover virtually [95]. Model Predictive Control, where the shared steering controller allows for balancing multiple goals (safety, efficiency, passenger comfort), and DT-based trajectory prediction have also been studied to decrease the chances of collisions due to inconsistent human operations such as aggressive/emergency lane change. This can support bus drivers in making quick decisions, especially in high-traffic situations [73]. Another study developed a traffic accident (TA) prediction system that uses a data service interface to connect with the business database, allowing it to access and retrieve business information to create a geographic information access view. Long Short-Term Memory (LSTM) network application to predict traffic accident time-series data can also be used for bus transit systems. By examining historical data related to specific bus routes and schedules, transit authorities can forecast potential accident-prone locations and times, enabling them to implement proactive safety measures [96]. Technological advancements can aid in real-time safety enhancements such as object detection, morion tracking, spatial attention, etc.