AI-Powered Traffic Control for Emergency Response

The AI-enabled Ambulance Routing and Traffic Management Framework represents a significant advancement in emergency medical services by combining artificial intelligence, IoT, and real-time communication to address the critical challenge of ambulance delays in congested urban areas. By integrating automated ambulance detection, intelligent traffic signal control, predictive ETA calculation, multi-ambulance coordination, and hospital notification systems, the framework ensures a seamless and efficient emergency response network. This system not only reduces travel time for ambulances but also enhances hospital preparedness, enabling timely medical intervention that can save lives. The AI-based ETA prediction and coordination modules improve decision-making, optimize resource allocation, and prevent route conflicts even in scenarios involving multiple ambulances. Furthermore, the framework demonstrates the potential of smart city technologies to create a connected and responsive urban healthcare ecosystem. With future enhancements such as autonomous vehicle integration, predictive traffic analysis, and patient monitoring, this system can be further developed to provide more accurate, adaptive, and scalable solutions for emergency services. Overall, the proposed framework contributes to reducing critical delays, improving patient survival rates, and establishing a safer and more intelligent urban environment, showcasing the transformative impact of AI-driven systems on public safety and healthcare management.

1.         Ahmad, I., & Kumar, A. (2020). IoT Enabled Emergency Vehicle Routing and Traffic Signal Control.      International    Journal     of            Engineering Research & Technology (IJERT), 9(9), 732–737. [IJERT] 2.         Bharadwaj, R., Deepak, J., Baranitharan, M., & Vaidehi, V. V. (2013). Efficient Dynamic Traffic Control System Using Wireless Sensor Networks. Proceedings of the International Conference on Recent Trends in Information Technology (ICRTIT). DOI: 10.1109/ICRTIT.2013.6844280. [IEEE] 3.         Chaitanya, D. R., & Anitha, T. (2019). Smart Ambulance System with Automatic Traffic Signal Control and Route Optimization. International Research Journal of Engineering and Technology (IRJET), 6(5), 4580–4585. [IRJET] 4.         Choudhury, K., & Nandi, D. (2021). Detection and Prioritization of Emergency Vehicles in Intelligent    Traffic    Management    System.    IEEE Bombay Section Signature Conference (IBSSC). DOI:10.1109/IBSSC53889.2021.9673211. [IEEE] 5.         Hancke, G. P., & Nellore, K. (2016). Traffic Management for Emergency Vehicle Priority Based on Visual Sensing. Sensors (Basel), 16(11), 1892. DOI: 10.3390/s16111892. [NIH] 6.         Memon, A. M., & Bhatti, N. (2020). AI-Based Smart Ambulance System for Dynamic Traffic Management. International Journal of Advanced Computer Science and Applications (IJACSA), 11(4), 7.         Nellore, K., & Melini, S. B. (2012). Automatic Traffic Monitoring System Using Lane Centre Edges. IOSR Journal of Engineering, 2(1), 1–8. [IOSRJEN] 8.         Patel, R., & Sharma, D. (2021). IoT-Based Smart Traffic Signal System with Emergency Vehicle Clearance.                 International                      Journal             of             Innovative Research in Computer and Communication Engineering (IJIRCCE), 9(4), 876–882. [IJIRCCE] 9.         Rajeshwari, S., Santhosh, K., & Balaji, S. (2017). Intelligent Traffic Control System for Ambulance Using RFID and GPS. International Journal of Computer Applications, 145(2), 0975–8887. [IJCA] 10.     Reddy, P. S., & Kumar, R. (2020). Machine Learning Based Emergency Vehicle Detection and Priority Management. IEEE Access, 8, 155200–155210. DOI: 10.1109/ACCESS.2020.3018756. [IEEE]