Abstract :

Traffic Jam And Slow Response To Emergency Situations Is Another Serious Issue In The Urban Transportation Systems That Has The Potential Consequence Of Higher Number Of Casualties In Medical And Fire Emergencies. In The Current Paper, A Deep Learning Approach To Detecting Emergency Vehicles In An Intelligenttraffic- Signal-control System Is Proposed, Implying That The System Should Not Be Developed With The Need To Install Additional IoT Devices. This Proposed System Works With A YOLO-based Convolutional Neural Network With OpenCV, Which Will Analyze Real-time Camera Feeds On A Traffic Camera And Identify Any Emergency Vehicles Like Ambulances, Fire Trucks, And Police Cars. Python And Streamlit/Flask Are Used To Implement The System In Realtime Monitoring And Control. After An Emergency Vehicle Has Been Detected, The Traffic Signal Will Automatically Turn To Green, So It Can Clear Quickly. Experimental Findings Show That The Detection Is Very Accurate, The Latency Is Low And The Performance Is Consistent Even In Varying Traffic Situations. The System Provides Cost-effective, Scalable Solution To Smart Cities Through Improving Efficiency Of Emergency Response And Decreasing Reliance On Manual Traffic Management.

Published:

07-5-2026

Issue:

Vol. 26 No. 5 (2026)

Page Nos:

332-337

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to Cite