In this work, we introduce a novel first-order nonlocal partial differential equation with saturated diffusion to describe the macroscopic behavior of traffic dynamics. We show how the proposed model is better in comparison with existing models in explaining the underlying driver behavior in real traffic data. In doing so, we introduce a methodology for adjusting the parameters of the proposed PDE with respect to the distribution of real datasets. In particular, we conceptually and analytically elaborate on how such calibration connects the solution of the PDE to the probability transition kernel proposed by the datasets. The performance of the model is thoroughly investigated with respect to several metrics. More precisely, we study the capability of the model in capturing the probability distribution realized by the datasets in the form of the fundamental diagram. We show that the model is capable of approximating the dynamics of the evolution of the probability distribution. To this end, we evaluate the performance of the model with regard to the congestion formation and dissipation scenarios from various datasets.