Traffic congestion in big cities has been proven to be a difficult problem with suboptimal effects in terms of driver delay and frustration, cost and impact on the environment. In principle, many transportation networks lack a unified framework, which will coordinate the traffic in such a manner, in order to suppress congestion and at the same time improve the travel time of the users situated in it. The rapid advancements in information, communication, and computation technologies have given rise to more elaborate modeling frameworks, aiming to act as the coordination unit necessary to counter the issue of congestion in real-time conditions. Such actions might have an adverse effect on the efficiency of the network, prospectively leading to greater waiting time intervals for each individual driver. We propose a macroscopic model equipped with an underlying reservation feature, known as Route Reservation Architecture (RRA). Vehicles enter the network (mainstream-wise or from the on-ramps) as long as this inflow does not incur a density that exceeds the network's critical density. Those vehicles prospectively exceeding the critical density are stored as queues at the origin of the motorway stretch and within the on-ramps Kotsialos and Papageorgiou (2004). Once there is sufficient space, for those vehicles to be accommodated by the respective cell of the network, they are discharged from their queueing instance at their respective origin, moving towards their assigned path freely. In previous works, this architecture has been only applied in microscopic simulations in the context of urban networks without the influence of source terms (on- ramps) Menelaou et al. (2017). When the critical density of the stretch is reached, the reservations are activated, instigating a waiting interval to the vehicles stored at queues, allowing vehicles only to enter the network at a later time instant, such that the critical density is not crossed. In this vein, we avoid the congested region and operate only at free-flow conditions. Our target is to investigate the effectiveness of this architecture, i.e. to minimize the Total Travel Time (TTT) of the vehicles present in the network, along with the vehicles situated in the queues, through a macroscopic simulation framework.