The present paper develops a Concensus-Based Decentralized expectation-Maximization (CB-DEM) algorithm to estimate the parameters of a mixture density model for use in distributed flow and speed modeling tasks performed with traffic data collected at spatially deployed traffic loop detector (traffic sensors) in a certain freeway network. This algorithm uses traffic measurements including volume, occupancy and mean speed which gathered by some inductive loop detectors. These traffic detectors (traffic sensors) located in certain distances in the freeway network such that they establish a Distributed Sensor Network (DSN). The convincing simulation results for a set field traffic data from the Metro Freeway Network provided by Regional Traffic Management Center (RTMC), a part of Minnesota Department of Transportation (MnDOT) are presented.