AI-Based Hybrid System for Profiling and Predicting Traffic Offenders

Road traffic violations remain a major challenge worldwide, contributing to accidents, injuries, and fatalities.Most existing traffic enforcement methods often rely on manual monitoring and static rule-based systems, which are inefficient in identifying repeat offenders and predicting future violations.This research proposes the design and implementation of a hybrid system for profiling and predicting traffic offenders using deep learning algorithms, aimed at enhancing law enforcement strategies and improving road safety.The system integrates unsupervised learning (K-Means clustering) for categorizing offenders into highrisk, medium-risk, and low-risk groups based on historical violation patterns, and supervised deep learning models (LSTMs) for predicting future offenses.By leveraging large-scale traffic data, the system enables proactive intervention by law enforcement agencies.The implementation utilizes Python, TensorFlow, and Scikit-learn libraries, with cloud-based infrastructure for real-time data processing and scalability.Performance evaluation using realworld traffic datasets demonstrates the system's effectiveness, with high accuracy in offender classification and future offense prediction.Compared to conventional enforcement techniques, the proposed AI-based hybrid approach enhances traffic monitoring, risk assessment, and predictive policing.This research contributes to advancing intelligent transportation systems, AI-driven law enforcement, and smart city initiatives, providing a scalable and automated framework for improving road safety.