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The Macroscopic Fundamental Diagram (MFD), which exhibits the relationship between average flow and average density of an urban network, is a promising framework for monitoring and controlling urban traffic networks. Given that monitoring resources (e.g. loop detectors, probe vehicle data, etc.) are limited in real-world networks, acquiring adequate data to estimate an MFD is of crucial importance. This study presents a novel, network-wide approach to identifying critical links and estimating average traffic flow and density. The proposed model estimates the MFD using flow and density measurements from those critical links, which constitute only a small subset of all the links in the network. To find the critical links, we rely on historical probe vehicle data, and propose a model that builds on Principal Component Analysis (PCA), a dimensionality reduction and a feature selection method. Essentially, using PCA, a …