Authors :
Sushmitha Gowda K R; N P Prajwal; Pramod B M; Nishanth A; Rohith V
Volume/Issue :
Volume 10 - 2025, Issue 5 - May
Google Scholar :
https://tinyurl.com/kubf5hfr
DOI :
https://doi.org/10.38124/ijisrt/25may1592
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Abstract :
In today’s growing urban traffic conditions, emergency vehicles like ambulances often get delayed due to heavy
congestion at intersections. Such delays can lead to critical consequences, especially in medical and rescue situations. This
project presents a Smart Traffic Management System that automatically detects emergency vehicles and gives them priority
by controlling traffic signals in real-time. The system uses an IP camera to continuously monitor traffic at a junction. Real-
time video frames are processed using object detection algorithms to accurately identify ambulances. Once an emergency
vehicle is detected, the system determines its direction of approach—North, South, East, or West. This direction data is
transmitted via the MQTT protocol to the traffic signal controller. The controller then immediately turns the traffic light
green in that direction while turning red for all other directions, allowing the ambulance to pass through without delay.
After the vehicle clears the junction, the system resets the signals back to normal operation. The entire process is automated
and requires no human intervention. This smart solution ensures faster emergency response, improves traffic management
efficiency, and reduces risks of accidents at intersections. The use of IoT, image processing, and automation makes the
system scalable and suitable for modern smart cities. Ultimately, this system is designed to save lives by minimizing delays
for emergency vehicles.
Keywords :
Emergency Vehicle Detection, Smart Traffic Control, Ambulance Priority System, Real-time Object Detection, Image Processing,IoT,MQTT Protocol, Traffic Signal Automation.Computer Vision,Smart City Solutions.
References :
- A. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You Only Look Once: Unified, Real-Time Object Detection,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), USA, 2016.
- K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” UK, 2015.
- M. Ali, F. A. Khan, and H. Sohail, “IoT-Based Smart Traffic Signal Control for Emergency Vehicle Priority,” UAE, 2022.
- R. Kumar, N. Singh, and P. Gupta, “Real-Time Ambulance Detection and Signal Manipulation Using AI and Embedded Systems,” India, 2023.
- A. Saxena and P. Roy, “YOLOv5-Based Vehicle Detection in Urban Smart Cities,” India, 2023.
- “ThingSpeak Documentation – IoT Cloud Platform,”MathWorks, https://thingspeak.com/docs/
- “MQTT Protocol – Lightweight Messaging,” MQTT.org, https://mqtt.org/
- “YOLOv5 Documentation – Ultralytics,” https://docs.ultralytics.com/
- “Smart Traffic Management System Using NodeMCU and ThingSpeak,” Electronics Hub, India, 2023
In today’s growing urban traffic conditions, emergency vehicles like ambulances often get delayed due to heavy
congestion at intersections. Such delays can lead to critical consequences, especially in medical and rescue situations. This
project presents a Smart Traffic Management System that automatically detects emergency vehicles and gives them priority
by controlling traffic signals in real-time. The system uses an IP camera to continuously monitor traffic at a junction. Real-
time video frames are processed using object detection algorithms to accurately identify ambulances. Once an emergency
vehicle is detected, the system determines its direction of approach—North, South, East, or West. This direction data is
transmitted via the MQTT protocol to the traffic signal controller. The controller then immediately turns the traffic light
green in that direction while turning red for all other directions, allowing the ambulance to pass through without delay.
After the vehicle clears the junction, the system resets the signals back to normal operation. The entire process is automated
and requires no human intervention. This smart solution ensures faster emergency response, improves traffic management
efficiency, and reduces risks of accidents at intersections. The use of IoT, image processing, and automation makes the
system scalable and suitable for modern smart cities. Ultimately, this system is designed to save lives by minimizing delays
for emergency vehicles.
Keywords :
Emergency Vehicle Detection, Smart Traffic Control, Ambulance Priority System, Real-time Object Detection, Image Processing,IoT,MQTT Protocol, Traffic Signal Automation.Computer Vision,Smart City Solutions.
