The Hierarchical Network-Based Method for Predicting Driver Traffic Violations

Traffic violations are a leading cause of road accidents and fatalities. Predicting driver violations in advance can help law enforcement agencies and traffic management systems take proactive measures to enhance road safety. This paper presents a Hierarchical Network-Based Method (HNBM) that integrates graph-based modeling, machine learning, and real-time data analytics to predict driver violations. The system constructs a hierarchical network by analyzing driver behavior, road conditions, historical violations, and traffic patterns. Machine learning techniques, such as Graph Neural Networks (GNNs), Random Forest, and Long Short-Term Memory (LSTM) networks, are applied to extract spatiotemporal features and predict the likelihood of violations. Experimental results on real-world traffic datasets demonstrate the model’s superior accuracy in identifying high-risk drivers compared to traditional approaches.