Abstract :

With The Rapid Increase In The Number Of Vehicles, Traffic Congestion Has Become A Major Issue In Urban Areas. Traditional Traffic Signal Systems Operate On Fixed Time Intervals, Which Often Leads To Inefficient Traffic Management, Increased Waiting Times, And Fuel Wastage. To Address This Problem, This Project Proposes A Density-based Smart Traffic Control System Using The Canny Edge Detection Algorithm For Real-time Traffic Analysis. The System Captures Live Video Or Images From Traffic Cameras And Processes Them Using Image Processing Techniques. The Canny Edge Detection Algorithm Is Applied To Detect Vehicle Edges And Estimate Traffic Density Based On The Number Of Detected Objects. Depending On The Traffic Density In Each Lane, The System Dynamically Adjusts Traffic Signal Timings To Optimize Vehicle Flow. The Implementation Is Carried Out Using Python And OpenCV For Image Processing, With Simulation And Testing Performed In A Controlled Environment. Experimental Results Show That The Proposed System Significantly Reduces Traffic Congestion And Waiting Time Compared To Traditional Methods. This Approach Provides An Efficient, Cost-effective, And Scalable Solution For Intelligent Traffic Management In Smart Cities.

Published:

08-4-2026

Issue:

Vol. 26 No. 4 (2026)

Page Nos:

1928-1934

Section:

Articles

License:

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

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