Track defects and failures resulting from weak strength and buckling cause many of the railroad accidents. One parameter that greatly affects track performance and safety is the track vertical modulus, which is a measure of the vertical stiffness of the rail foundation. Current techniques for track modulus measurement, while accurate, are time-consuming, labor-intensive, require track closure, and provide only single-point information. Ground penetrating radar (GPR) is a non-destructive and non-invasive technology that has shows great potential for imaging subsurface features and assessing the integrity of track substructure. The technique is based on the principles of electromagnetic wave reflection from and transmission through distinct layers of varying dielectric properties. A single electromagnetic pulse of energy at an appropriate frequency is launched into the ground, and reflections from various subsurface layers are recorded in the form of an image. Since studies have shown that the electrical properties of base course aggregates can be used to infer their strength properties, we hypothesize that the information on the track substructure layering characteristics can be used to indirectly infer track modulus. Using a comprehensive set of coincident GPR and track modulus measurements acquired over various types of railroad track geometries, a multivariate linear regression model has been developed. Our analysis reveals a consistent relationship between the weighted average of subsurface 400 MHz GPR reflectivities at specific depths to the measured track modulus with an accuracy of better than approximately 3.4 MPa (500lb/in./in.). The model is thus able to predict track modulus from GPR measurements, and would considerably reduce the time and expense of operational track maintenance strategies.