Priority–Based Traffic Signal System for Ambulances

International Research Journal of Engineering and Technology (IRJET) Volume: 12 Issue: 07 | Jul 2025 www.irjet.net

e-ISSN: 2395-0056 p-ISSN: 2395-0072

Priority–Based Traffic Signal System for Ambulances 1Priyanka Bhondve, 2Mayuri Desai, 3Suhani Kasliwal, 4Prof. Mrs. Sanjyot Thuse 123Student, 4Associate Professor

1234Electronics and Telecommunication, 1234P. E. S. Modern College of Engineering, Pune, India

------------------------------------------------------------------------***---------------------------------------------------------------------Abstract—The system captures live traffic footage and processes it to identify ambulances accurately. Upon

detection, a signal is sent to the traffic control system, allowing automatic adjustments to traffic lights, ensuring a clear passage for the emergency vehicle. The integration of machine learning enhances detection accuracy, making the system efficient and reliable. By implementing this technology, response times for emergency services can be significantly improved, reducing delays caused by traffic congestion. This system has the potential to be integrated into smart city infrastructure, enhancing urban traffic management and emergency response efficiency.

Keywords – traffic, ambulance, emergency, detection, management

I. INTRODUCTION Traffic congestion in urban areas has become a growing concern, particularly in metropolitan cities where road networks are often overburdened. One of the most critical consequences of traffic congestion is the delay caused to emergency vehicles, such as ambulances. In life-threatening situations, every second counts, and any delay in reaching the hospital can have severe consequences for patients in critical condition. Conventional traffic control systems operate on fixed timing sequences and do not adapt to emergency requirements, leading to significant delays for ambulances stuck in traffic. To address this issue, an intelligent traffic control system is required that can dynamically manage traffic signals based on real-time ambulance detection.

This project, Priority-Based Traffic Signal System for Ambulances, aims to develop a smart traffic management system that can automatically detect ambulances and adjust traffic signals accordingly. The system utilizes a Raspberry Pi as the primary computing unit, interfaced with a camera module to capture real-time traffic footage. The video feed is processed using a Convolutional Neural Network (CNN) algorithm, which is trained to accurately recognize ambulances within the traffic. Once an ambulance is detected, the system sends a signal to the traffic control unit, allowing the traffic light to change in favor of the emergency vehicle, thereby ensuring a clear passage through congested areas.

The core functionality of the project relies on machine learning-based image processing. CNN, a powerful deep learning model, is used for real-time object detection, ensuring accurate recognition of ambulances from the video feed.

The Raspberry Pi processes the captured images, runs the trained CNN model, and communicates with the traffic light controller. By integrating these technologies, the system is capable of making real-time decisions, significantly reducing delays for emergency vehicles.

The proposed system offers several advantages over traditional traffic management methods. Unlike conventional fixed-timer traffic signals, this system is adaptive and responsive, making it more efficient in handling emergency situations. It eliminates the need for human intervention, reducing manual errors and increasing reliability. Furthermore, the system can be integrated into smart city infrastructures, enhancing overall urban traffic management and emergency response efficiency.

The successful implementation of this project has the potential to save lives by reducing ambulance response times and ensuring timely medical attention for patients in need. With continuous advancements in artificial intelligence and embedded systems, this approach can be further enhanced by integrating vehicle-to-infrastructure (V2I) communication, IoT-based data exchange, and additional machine learning optimizations. This project lays the foundation for a more intelligent and responsive traffic management system, contributing to safer and smarter cities in the future.

II. Objective This project aims to develop a real-time ambulance detection system using computer vision and deep learning techniques to enhance emergency response efficiency. A Convolutional Neural Network (CNN) model will be implemented to accurately identify ambulances from live traffic footage. The system will integrate a Raspberry Pi-based embedded solution for processing image data and controlling traffic signals, enabling automated signal adjustments to prioritize

© 2025, IRJET

|

Impact Factor value: 8.315

|

ISO 9001:2008 Certified Journal

|

Page 640

International Research Journal of Engineering and Technology (IRJET) Volume: 12 Issue: 07 | Jul 2025 www.irjet.net

e-ISSN: 2395-0056 p-ISSN: 2395-0072

ambulance movement. By minimizing manual intervention in traffic management, the project seeks to reduce human errors and response delays. Additionally, the system is designed to be cost-effective and scalable, ensuring seamless integration into existing traffic infrastructure. Ultimately, this project contributes to smart city traffic management by optimizing emergency vehicle movement and improving road usage efficiency.

III. Block Diagram

Fig. 1: Block Diagram

IV. Proposed System 1. Camera Module: The camera captures real-time images of the road and approaching vehicles.

It acts as the primary input device, providing image frames to the Raspberry Pi for processing.

A high-resolution USB webcam or a Raspberry Pi Camera Module is used for accurate image acquisition.

2. Input as Image: The captured images from the camera are converted into digital form and sent as input to the Raspberry Pi. These images are then processed using computer vision techniques before being analyzed by the deep learning model.

3. Raspberry Pi (Main Processing Unit): This is the core processing unit of the system, responsible for ambulance detection and signal control.

It performs several operations, including image preprocessing, feature extraction, and classification using a Convolutional Neural Network (CNN) algorithm.

© 2025, IRJET

|

Impact Factor value: 8.315

|

ISO 9001:2008 Certified Journal

|

Page 641

International Research Journal of Engineering and Technology (IRJET) Volume: 12 Issue: 07 | Jul 2025 www.irjet.net

e-ISSN: 2395-0056 p-ISSN: 2395-0072

Based on the detection results, it decides whether to override the traffic signal to allow ambulance passage.

4. Preprocessing: Image preprocessing enhances the quality of captured images for better analysis.

It includes operations such as grayscale conversion, noise reduction, resizing, and contrast adjustments.

This step ensures that the CNN model receives clear and optimized images for feature extraction.

5. Feature Extraction: In this step, important features such as ambulance shape, color, and emergency siren patterns are extracted.

The CNN model processes these features to distinguish an ambulance from other vehicles.

This ensures a high detection accuracy under various lighting and traffic conditions.

6. CNN Algorithm Working: A trained deep learning model (CNN) is used to classify whether the detected vehicle is an ambulance or a regular vehicle.

The CNN model consists of multiple layers, including convolutional layers, pooling layers, and fully connected layers, which analyze patterns in the images.

Once trained, the model can identify ambulances with high accuracy and make real-time decisions.

7. Output – Ambulance Detected or Not: If an ambulance is detected, a signal is sent to override the traffic light system, giving it the highest priority.

If no ambulance is detected, normal traffic light operations continue as per standard timing cycles.

8. Traffic Signal Control: The Raspberry Pi controls the traffic signal based on ambulance detection results.

If an ambulance is detected, the red light is overridden, turning the signal green for the ambulance to pass.

Once the ambulance crosses the intersection, the system resumes normal traffic light operations.

9. Power Supply The Raspberry Pi 4 can be powered through a USB port on the PC using a USB-C cable.

The PC provides 5V DC power to the Raspberry Pi, ensuring stable operation.

The camera module and other connected components also receive power through the Raspberry Pi’s GPIO or USB ports The PC can also process and display outputs if needed, providing an interface for monitoring the system.

© 2025, IRJET

|

Impact Factor value: 8.315

|

ISO 9001:2008 Certified Journal

|

Page 642

International Research Journal of Engineering and Technology (IRJET) Volume: 12 Issue: 07 | Jul 2025 www.irjet.net

e-ISSN: 2395-0056 p-ISSN: 2395-0072

V. Flowchart

VI. Conclusion The development of a real-time ambulance detection system using computer vision and deep learning offers a significant advancement in traffic management and emergency response. By implementing a Convolutional Neural Network (CNN) for accurate ambulance identification and integrating a Raspberry Pi-based embedded system for automated traffic signal control, this project ensures priority clearance for emergency vehicles. The automation of traffic signals minimizes human intervention, reducing errors and delays while improving overall road efficiency. Additionally, the system is designed to be cost-effective and scalable, making it a viable solution for modern smart city infrastructure. Ultimately, this project enhances emergency response times, optimizes traffic flow, and contributes to a safer and more efficient urban transportation network.

VII. Acknowledgement We extend our heartfelt gratitude to everyone who contributed to the successful completion of our project, ‘PriorityBased Traffic Siignal System For Ambulances’. We are deeply appreciative of Mrs. Sanyjot Thuse, whose unwavering guidance, expertise, and encouragement have been instrumental in shaping this project. Her insightful feedback and constant support have greatly enriched our understanding and helped us navigate challenges effectively. Our sincere gratitude goes to Principal K. R. Joshi for her steadfast support and belief in the importance of innovative projects like this.

Her encouragement and leadership provided us with the motivation to pursue this endeavor with dedication.

We also express our appreciation to the Electronics and Telecommunication Department, particularly Dr. Mrs. R. S. Kamathe, for her technical expertise and invaluable assistance throughout the course of this project. The resources and guidance provided by the department played a vital role in its successful execution.

VIII. References [1] Janani Saradha, G.Vijayshri, T. Subha “Intelligent Traffic control System for Ambulance using RFID and CLOUD. “IEEE Second International Conference On computing and Communication echnologies (ICCT‟17) 2017 18-08-2017, 15.14 [2] P.Shiny, I.Thasneem Banu, B.Thenmozhi, U.T.Sasikala & Dr.M.Ramkumar Prabhu “Intelligent Traffic Control System for Congestion Control using Image Processing, Ambulance Clearance, and Stolen Vehicle Detection”. “International Journal of Advanced Reasearch Trends in Engineering And Technology(IJARTET)2016” 20-08- 2017, 09.00

© 2025, IRJET

|

Impact Factor value: 8.315

|

ISO 9001:2008 Certified Journal

|

Page 643

International Research Journal of Engineering and Technology (IRJET) Volume: 12 Issue: 07 | Jul 2025 www.irjet.net

e-ISSN: 2395-0056 p-ISSN: 2395-0072

[3] Prashant Jadhav, Pratiksha Kelkar, Kunal Patil, Snehal Thorat “Smart Traffic Control System using Image Processing”.

“International Research Journal of Engineering And Technology(IRJET) 2016”. 25-08 2017, 17.00 [4] Arif A. Bookseller, Rupali R. Jagtap “Image processing based adaptive traffic control system”.”IOSR Journal of Electronics and Communication Engineering(IOSR- JECE)”.30-08-2017, 19.30 [5] Pandiaraj, K., et al. "RFID Based Automatic Lane Clearance for Ambulance." 2021 International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE). IEEE, 2021.

[6] Deepajothi, S., et al. "Intelligent Traffic Management for Emergency Vehicles using Convolutional Neural Network."

2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS). Vol. 1. IEEE, 2021 [7] Kamdar, Arihant, and Jigarkumar Shah. "Smart traffic system using traffic ow models." 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS). IEEE, 2021.

[8] Rachana K P, Aravind R, Ranjitha M, Spoorthi Jwanita, Soumya K, 2021, IOT Based Smart Traffic Management System, INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH & TECHNOLOGY (IJERT) NCCDS – 2021 (Volume 09 – Issue 12), [9] V. Bali, S. Mathur, V. Sharma and D. Gaur, "Smart Traffic Management System using IoT Enabled Technology," 2020 2nd International Conference on Advances in Computing, Communication Control and Networking (ICACCCN), 2020, pp.

565-568, doi10.1109/ICACCCN51052.2020.9362753.

[10] Lingani, Guy M., Danda B. Rawat, and Moses Garuba. "Smart traffic management system using deep learning for smart city applications." 2019 IEEE 9th annual computing and communication workshop and conference (CCWC). IEEE, 2019.

[11] Javaid, Sabeen, et al. "Smart traffic management system using Internet of Things." 2018 20th international conference on advanced communication technology (ICACT). IEEE, 2018 [12] Ghazal, Bilal, et al. "Smart traffic light control system." 2016 third international conference on electrical, electronics, computer engineering and their applications (EECEA). IEEE, 2016.

[13] Lanke, Ninad, and Sheetal Koul. "Smart traffic management system." International Journal of Computer Applications 75.7 (2013) [14] Smart Traffic Control System for Emergency Vehicle Clearance, D.Aswani, Student, Department of ECE, C. Padma, (Ph.D), Assistant Professor, Department of ECE, Priyadarshini Institute of Technology, Ramachandrapuram, Tirupati, A.P., India.

© 2025, IRJET

|

Impact Factor value: 8.315

|

ISO 9001:2008 Certified Journal

|

Page 644