Ai-based Smart Traffic Signal Control System For Emergency Vehicle Prioritization

• Author(s):

  Aishwarya

• Keywords:

  Ambulance Detection, Intelligent Traffic Signal Control, Emergency Vehicle Priority, Computer Vision, Deep Learning, YOLOv8, Smart Transportation Systems

• Abstract:

  The Rapid Growth Of Urbanization Has Significantly Increased Traffic Congestion, Posing Critical Challenges For Emergency Medical Services, Particularly Ambulances. Delays Caused By Conventional Fixed-time Traffic Signal Systems Can Lead To Increased Response Times And Adverse Outcomes For Patients. This Situation Highlights The Need For Intelligent, Automated, And Real-time Traffic Management Solutions Capable Of Prioritizing Emergency Vehicles. This Paper Presents An AI-Based Smart Traffic Control System For Emergency Vehicle Prioritization Using Deep Learning And Computer Vision Techniques. The Proposed System Employs A Custom-trained YOLOv8 Model To Accurately Detect Ambulances From Real-time Or Recorded Traffic Camera Feeds. The Detection Model Is Trained Using A Labeled Dataset Prepared With Roboflow, Ensuring High Precision For Ambulance-specific Classification. Upon Detecting An Ambulance With Sufficient Confidence, The Traffic Signal Dynamically Switches To A Green Phase, Allowing Uninterrupted Passage Through The Intersection. To Ensure System Stability And Prevent False Triggering, A Timeout-based Control Mechanism Is Incorporated, Enabling The Signal To Revert Safely To Normal Operation Once The Ambulance Exits The Camera’s Field Of View. A Graphical User Interface Is Developed To Visually Represent Traffic Signal States And Emergency Conditions, Providing Real-time Monitoring And Transparency. The System Is Implemented Using Python, OpenCV, And A Multithreaded Architecture To Maintain Real-time Performance Without Blocking The User Interface. Experimental Evaluation Demonstrates Reliable Ambulance Detection, Smooth Signal Transitions, And Effective Prioritization Under Varying Traffic Conditions. The Proposed Solution Enhances Emergency Response Efficiency, Reduces Manual Intervention, And Offers A Scalable Framework That Can Be Integrated Into Smart City Traffic Infrastructure. This Work Contributes Toward Improving Urban Emergency Mobility Through Intelligent Automation And AI-driven Traffic Management.

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Other Details

• Paper id:

  IJSARTV12I3104634

• Published in:

  Volume: 12 Issue: 3 March 2026

• Publication Date:

  2026-03-04

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