This project evaluates the effectiveness of sensor network systems for work zone traffic estimation. The comparative analysis is performed on a work zone modeled in microsimulation and calibrated with field data from two Illinois work zones. Realistic error models are used to generate noisy measurements corresponding to Doppler radar sensors, remote traffic microwave sensors (RTMS), and low-energy radar (LER). The velocity, queue length, and travel time are estimated with three algorithms based on (1) interpolation, (2) spatio-temporal smoothing, and a (3) flow model–based Kalman filter. More than 700 sensor and algorithm configurations are evaluated, and the accuracy of the resulting traffic estimates are compared with the true traffic state from the microsimulation. The nonlinear Kalman filter provides up to 30% error reduction over other velocity estimators when the RTMS spacing exceeds 2 miles and generally offers the best performance for queue and travel time estimation.