TrafficViolationNet: Data-Driven Traffic Violation Prediction Model Based on Deep Dual-Path Residual Network and Lightweight Attention Mechanism

Traffic violations constitute one of the principal contributors to serious road accidents and remain a persistent threat to public safety and property worldwide. For this reason, accurate identification and prediction of traffic violations are of considerable importance for improving traffic governance and supporting early intervention. To address the challenges posed by traffic violation data with complex structures and heterogeneous feature distributions, this paper proposes a new classification framework: TrafficViolationNet. The proposed model integrates an enhanced residual architecture with a lightweight attention mechanism to improve feature learning from structured traffic data. At the architectural level, TrafficViolationNet is built upon ResNet Plus, in which auxiliary residual branches and dense shortcut connections are introduced to facilitate information propagation, improve gradient flow, and strengthen feature representation. In addition, an attention module is incorporated into each residual block to adaptively emphasize informative features and capture complex dependencies among variables. Experimental results on traffic violation datasets from the United States and Qatar show that the proposed method consistently outperforms mainstream machine learning baselines and achieves state-of-the-art classification performance.