Mobility and transportation are two of the leading indicators of economic growth of a society. As cities around the world grow rapidly and more people and modes compete for limited urban space to travel, there is an increasing need to understand how this space is used for transportation and how it can be managed to improve accessibility for everyone. In a recent paper, Daganzo and Geroliminis explored the connection between network structure and a network’s MFD for urban neighborhoods with cars controlled by traffic signals and derived an analytical theory for the MFD using Variational Theory. Information needed to estimate this network MFD’s are average network (total length of roads in lane-km, number of lanes, length of links), control (signal offsets, green phase and cycle time) and traffic (free flow speed, congested wave speed, jam density, capacity) characteristics. However in previous studies, Variational Theory has been applied only in cities with deterministic values of the above variables for the whole network and by ignoring the effect of turns. In our study we are aiming to generate an MFD for streets with variable link lengths and signal characteristics and understand the effect of variability for different cities and signal structures. Furthermore, this variability gives the opportunity to mimic the effect of turning movements and heterogeneity in drivers’ behavior. This will be a key issue in planning the signal regimes such a way that maximizes the network capacity and/or the density range of the capacity