Efficient traffic control and management of large-scale transportation networks still remain a challenge both for traffic researchers and practitioners. Over decades, control strategies have been proposed and applied for isolated or coordinated intersections in arterials.
In this microscopic level, a novel research direction is to derive optimal control strategies for signalized intersections during undersaturated and oversaturated conditions, and they can be applied in real-time with queue length feedback control if queue length sensors are available. These strategies usually utilize disaggregate traffic flow models, as behavior of each vehicle is modeled in detail, e.g. car following and lane changing models. However, modeling the traffic flow dynamics of each element in a large-scale network with a large number of links and intersections is a complex task. One has to model the evolution of queues at each signalized intersection, and to account for the queue dynamic interactions between adjacent intersections. Hence, instead of this approach, the macroscopic fundamental diagram (MFD) aims at simplifying the micro-modeling task of the urban network where the collective traffic flow dynamics of subnetworks capture the main characteristics of traffic congestion, such as the evolution of space-mean flows and densities in different regions. The MFD provides a unimodal, low-scatter relationship between network vehicle density (veh=km) and network space-mean flow or outflow (veh=hr) for different network regions, if congestion is roughly homogeneous in the region. The MFD can be utilized to introduce elegant real-time control strategies to improve mobility and decrease delays in large urban networks, that local ones are unable to succeed.
