This paper presents an optimized reduction of additive energy consumption for traffic signals using a suppressed superposition framework. This approach combines the improved performance of the superposition framework with a systematic process for suppressing energy usage. By using a mathematical model that eliminates the effect of parametric variations in traffic signals, the proposed method can reduce the energy-cost by suppressing unnecessary superposition consumption which results in an optimized energy consumption in traffic signals. The method isolates unique parameters of each signaling experience to reduce unnecessary overlay energy costs and provide precise reductions in total energy consumption. This framework leverages the mutual information between traffic light sources and their corresponding targets, further reducing the additive energy consumption of traffic lights. In addition, a model-prediction-based broadcasting scheduling approach is proposed to reduce the number of broadcasts for each traffic light. It also presents an evaluation of the model in terms of energy savings, code reliability, robustness, and scalability. The proposed method outperforms the traditional approach in terms of efficiency and simplicity that makes it more suitable for large-scale traffic signal control in urban areas.