Violation behaviors of electric bicycles at signal-controlled intersections have become an increasingly serious safety hazard in urban traffic systems, posing a significant threat to traffic order and safety. To achieve refined assessment and dynamic intervention of such violations, this study proposes an analytical method that integrates survival analysis with machine learning to reveal the temporal evolution patterns and key driving factors of electric bicycle violation behaviors. First, by combining the Kaplan–Meier method and the Cox proportional hazards model, a violation rate function for electric bicycles is established to analyze the temporal characteristics of violation behaviors. Second, a lightweight ensemble learning model, LightGBM, based on the gradient boosting framework, is introduced to develop a violation behavior inference approach. The SHAP analysis is further employed to enhance model interpretability, enabling a comprehensive examination of electric bicycle violation behaviors at signalized intersections and exploring the combined effects of multidimensional environmental and individual characteristics on violation risk. Finally, an empirical study based on nearly 600 samples extracted from video recordings is conducted. The results of the electric bicycle violation rate function indicate that secondary crossing, right-turn conflicts, and rider identity significantly increase the probability of violations. Riders carrying passengers exhibit a lower probability of violation, with a violation risk equal to 85% of that of non-passenger riders. Yielding behavior markedly reduces the probability of violation, lowering the violation risk by approximately 54%. Owing to the gradient boosting optimization strategy, the interpretable LightGBM model proposed in this study achieves higher accuracy in violation behavior inference and significantly faster training speed than models such as random forests, demonstrating its superiority in assessing the violation risks of electric bicycles. The model results further show that following behavior, observation of cross traffic, secondary crossing, and group size are the core factors influencing violation behaviors. Following behavior triggers a “herd effect,” and an increase in group size strengthens the sense of “deindividuation,” leading to a significant rise in violation probability as the group expands. When riders travel in groups of more than three, individual responsibility weakens, and a “law does not punish the crowd” mentality emerges, substantially increasing the violation risk. The findings provide theoretical support for the refined governance of electric bicycle violation behaviors and the development of more precise traffic management policies.