This paper develops an automatic method to identify pavement layer properties from ground penetrating radar (GPR) data collected at traffic speed. The GPR system is operated at a center frequency of 2 GHz with a penetration depth of 60 cm in common road materials. Features include the capability of collecting up to 1000 traces/s, a large dynamic range, and compacted packaging. Using a four-channel GPR system, a large amount of data are collected at traffic speed on urban roads for over 200 lane miles, providing a dense spatial coverage. The GPR data contain information about the pavement layer properties, including layer interface, dielectric constant, and layer thickness. Using cross correlation and Hilbert transform algorithms, the pavement layer properties are identified from the large GPR data sets automatically and efficiently. The method has been successfully demonstrated in engineering applications for the accurate estimation of the layer thickness with excellent repeatability. Moreover, thickness data from different radar channels at the same location are used for transversal profile prediction. By searching abnormal variations of layer properties and amplitude of reflection signals, features and/or possible distresses in surface and subsurface, such as full-depth asphalt patches and prepothole conditions, can be detected.