In this work, consensus Alternating Direction Method of Multipliers (ADMM) was implemented with the intention of improving the performance of Model Predictive Control (MPC) on an automobile traffic network. Automobile traffic networks must be engineered to be as efficient as possible in order to mitigate their contributions to climate change. In addition to global climate implications, more efficient traffic networks can benefit individual consumers. This work examines methods for controlling traffic networks with digital speed limits to enable these positive outcomes. Beyond practical interests, traffic network control is of theoretical interest because of the potential to apply traffic control schemes on other systems. MPC is one method for traffic network control. The main benefits of MPC in this application are that it can constrain inputs and handle disturbances. MPC is good for handling disturbances because it predicts future system behavior to inform control decisions. However, this also means that MPC can be computationally expensive. This work attempts to combat the computational expense by implementing consensus ADMM within the MPC optimization. Consensus ADMM will be used to break the computation down into smaller pieces that overlap in their effective range before recombining them into one solution. Subsequently, this method should lighten the computational load. In the context of traffic networks, a length of road will be broken down into multiple sections which can be referred to as "agents." An agent's control command will be the ideal speed limit(s) within its effective range. In this work, MPC and basic ADMM are first applied on a translational cart system. In the next case study, consensus ADMM will be implemented on a multi-agent, interconnected version of this cart system. Lastly, MPC with consensus ADMM will be applied to a traffic network simulation.