## 4 Use Cases and Application Scenarios of Internet of Emergency Services
### Smart Transportation  
The Smart Transportation scenario in the Internet of Emergency Services (IoES) refers to the use of emerging technologies and data-driven approaches to enhance the safety and efficiency of transportation systems during emergency situations [48; 68; 132; 148]. This scenario involves the integration of various components such as sensors, communication networks, data analytics, and decision support systems to facilitate the real-time monitoring and coordination of emergency responses in the transportation sector. One example of the application of IoES in smart transportation is the use of connected vehicles and intelligent transportation systems (ITS) to support the emergency response in the event of a disaster or crisis. By leveraging data from various sensors, such as GPS, cameras, and weather sensors, connected vehicles and ITS can provide real-time information on road conditions and traffic flow, allowing emergency responders to make informed decisions and allocate resources more effectively. For instance, during Hurricane Harvey in 2017, the Texas Department of Transportation used ITS technology to monitor traffic and road conditions, provide real-time traffic updates to the public, and coordinate emergency responses. The department also deployed drones equipped with high-resolution cameras to capture images and videos of the flooding, which were used to assess damage and plan recovery efforts. Another example of the application of IoES in smart transportation is the use of autonomous vehicles for the emergency response. Autonomous vehicles can be deployed  
Figure 7: Public Safety scenario. to transport patients and medical supplies during emergencies, reducing the response time and increasing the efficiency of emergency services. Moreover, autonomous vehicles can operate in hazardous conditions where human drivers would be at risk, such as in the case of chemical spills or nuclear accidents. For instance, in 2017, the American Red Cross and the Virginia Department of Transportation conducted a pilot project to test the use of autonomous vehicles for the emergency response. The project involved the deployment of a self-driving shuttle equipped with medical supplies and communication equipment to assist with the emergency response in rural areas. Further, we discuss an example of the Smart Transportation scenario (Figure 8) in IoES:  
1. A self-driving car detects a sudden obstacle on the road and is unable to avoid a collision. 2. The car's sensors send an emergency signal to the transportation management center (TMC), which receives the alert in real-time. 3. The TMC operator immediately reviews the situation and identifies the location of the accident. 4. The TMC dispatches the nearest emergency vehicle, such as an ambulance or a fire truck, to the accident site. 5. The TMC also sends alerts to nearby drivers, informing them of the accident and suggesting alternate routes to avoid the area. 6. The TMC operator accesses real-time traffic data to adjust traffic lights and optimize traffic flow around the accident site, ensuring that emergency vehicles can reach the scene quickly. 7. Emergency responders arrive at the scene, and the injured are transported to the nearest hospital. 8. The TMC updates the status of the accident on its public-facing platform to keep citizens informed about any disruptions or delays. 9. The TMC sends the accident data to the city's transportation and emergency services departments for analysis, allowing them to identify any patterns or trends in accident data and take action to improve road safety. This scenario demonstrates how IoES can enable real-time response to accidents and emergencies on the road, leveraging connected vehicles, intelligent transportation systems, and emergency services to ensure a fast and effective response to incidents. The Smart Transportation scenario in IoES presents several opportunities to enhance the safety and efficiency of transportation systems during emergency situations.