Abstract :

Traffic Violations Are A Major Contributor To Road Accidents, Congestion, And Loss Of Human Life. Traditional Traffic Monitoring Systems Mainly Rely On Manual Enforcement And Rule-based Detection, Which Are Often Reactive Rather Than Preventive. With The Increasing Availability Of Traffic And Driver Behavior Data, There Is A Growing Need For Intelligent Systems That Can Predict Violations Before They Occur. This Work Proposes A Smart Prediction Framework That Models Drivers As Interconnected Entities Within A Behavior Network. By Analyzing Driving Patterns, Historical Violations, And Interaction Relationships, The System Identifies High-risk Drivers And Predicts Potential Traffic Violations. The Proposed Approach Improves Early Detection, Enhances Road Safety, And Supports Proactive Traffic Management.

Published:

06-7-2026

Issue:

Vol. 26 No. 7 (2026)

Page Nos:

1592-1596

Section:

Articles

License:

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

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