• Deeksha S
• Disha A
• Gangothri P
• Sakshi Singh
• Damera Saritha

Traffic congestion is an everyday problem in today's urban settings, with grave implications particularly for emergency vehicles such as ambulances, fire engines, and police vehicles. These vehicles end up stuck in traffic as a result of inflexible signal patterns and insensitive traffic systems, leading to slow emergency response that can mean lives lost. To address this problem, intelligent traffic management systems have been suggested by researchers across a broad spectrum to give priority to emergency vehicles and reach their destinations without wasting any time. This literature review investigates some new trends over the last few years based on image processing and machine learning methodologies like Convolutional Neural Networks (CNNs) and the YOLO (You Only Look Once) algorithm for real-time identification of ambulances from videos. By using an ESP32-CAM for real-time video input and YOLOv8 for object detection, ambulances can be automatically recognized. Upon detection, the system communicates with traffic lights to turn green in the ambulance’s path. In addition, there is a companion mobile app for the ambulance driver to enter their location and destination hospital. The program determines the shortest path based on GPS and liaises with the next traffic lights to clear the route ahead of time, which generates an active green pathway. This can dramatically improve emergency response times and even save lives.

Emergency, traffic ,ambulance, app, awareness ,shortest path, gps

"EMERGE-Emergency vehicle Mobility Efficiency Routing Guidance Enhancement", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 5, page no.j760-j763, May-2025, Available :http://www.jetir.org/papers/JETIR2505A67.pdf

"EMERGE-Emergency vehicle Mobility Efficiency Routing Guidance Enhancement", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 5, page no. ppj760-j763, May-2025, Available at : http://www.jetir.org/papers/JETIR2505A67.pdf