Smart Prediction Of Traffic Violations Using Driver Behavior Networks
DOI:
https://doi.org/10.46647/rdems0204042Keywords:
Traffic violation prediction, Driver behavior analysis, Machine learning, Deep learning, Intelligent transportation systems (ITS), Behavioral modeling, Neural networks, Traffic safety, Data analytics, Anomaly detection, Predictive modeling, Road safety systems, Vehicular data analysis, Smart city applications, Classification algorithms.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.