Abstract :

With The Rapid Growth Of Urbanization And Increasing Number Of Vehicles On Roads, Efficient Traffic Management Has Become A Critical Challenge For Modern Cities. Traditional Traffic Monitoring Systems Rely On Manual Observation Or Basic Sensor-based Methods, Which Often Lack Accuracy And Real-time Adaptability. This Project Proposes A MultiTraffic Sense Perception System Based On Supervised Learning To Improve Traffic Monitoring, Analysis, And Decision-making. The Proposed System Utilizes Supervised Machine Learning Algorithms To Analyze Traffic Data Collected From Multiple Sources Such As Cameras, Sensors, And IoT Devices. It Processes Visual And Numerical Data To Detect Traffic Density, Vehicle Types, Congestion Levels, And Abnormal Events Such As Accidents Or Violations. Image Processing Techniques Combined With Classification Models Like Support Vector Machine (SVM), Decision Trees, And Convolutional Neural Networks (CNN) Are Used To Accurately Identify Traffic Patterns. The Supervised Learning Approach Enables The System To Learn From Labeled Datasets, Improving Prediction Accuracy Over Time. Experimental Results Demonstrate That The System Achieves High Accuracy In Traffic Classification And Congestion Detection. It Provides Real-time Insights That Can Be Used For Traffic Control, Route Optimization, And Smart City Planning. However, Challenges Such As Data Quality, Environmental Conditions, And Computational Complexity Remain. The Proposed System Offers A Scalable And Efficient Solution For Intelligent Traffic Management, Contributing To Safer And More Organized Transportation Systems.

Published:

08-4-2026

Issue:

Vol. 26 No. 4 (2026)

Page Nos:

1899-1905

Section:

Articles

License:

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

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