Virtual Booth

Every second counts when an ambulance is stuck in traffic. In high-density urban corridors like Johor Bahru, emergency vehicles frequently face critical delays caused by congested intersections. These delays can cost lives within the medical "Golden Hour," which is the vital window where timely intervention most significantly improves patient survival. Existing smart traffic systems can solve this problem, but their high deployment costs make widespread adoption unrealistic for most municipal councils. The EZSSI Smart Road Traffic Management System is an AI-driven traffic solution developed to close this gap. Using an ESP32 microcontroller paired with a computer vision model trained on Google's Teachable Machine, the system detects approaching ambulances and triggers automatic green-light preemption within a mere 120 milliseconds. Tested across diverse real-world image datasets, it achieves an impressive 90% detection accuracy without requiring any modifications to existing vehicles or road infrastructure. To validate real-world applicability, I collaborated with St. John Ambulans Malaysia Bahagian SMK Sultan Ismail , whose emergency response team confirmed that the system's workflow aligns perfectly with actual on-ground operations. At an estimated deployment cost of RM5,000 per intersection, it offers a practical and scalable alternative within a global traffic management market projected to reach USD 131.50 billion. Our solution directly supports UN Sustainable Development Goals 9 and 11, proving that life-saving smart city technology does not have to come with a life-size price tag.