With increasing realised traffic on transport networks, greenhouse gas emissions show a similar trend. Reducing them is a modern aspiration, creating a better place to live and moving towards sustainability. Expanding the infrastructure is often not an appropriate solution, as the system would only be fully utilised at peak times, while at less frequent times it would not even approach capacity and would require huge investment costs. An alternative to further construction work is the implementation of intelligent traffic systems, where smoother flows can achieve higher capacity by reducing the variability in the system. In a motorway environment, a common approach is Variable Speed Limit Control, where the road is divided into zones and individual speed limits are used to increase or decrease the load on the cells. This paper proposes a solution in which individual cells make decisions cooperatively, in contrast to classical state machine-based methods. Thanks to the jointly formulated goal of the agents, a predictive control method is created that leads to a reduction in emissions due to avoided shock waves and reduced waiting times. This paper presents a solution that provides a universal solution across multiple application lengths, illustrating the power of deep learning.