U.S. railroads transport 1.6 billion tons of freight over more than 140,000 miles of track each year. Safe and efficient operation of such a vast infrastructure requires extensive monitoring, evaluation, and maintenance of its track systems. Track modulus is a critical parameter in the design, analysis, and maintenance of railroad track, as it is indication of  the material stiffness below the rail comprising the combined stiffnesses per unit length of rail, plates, ties, ballast, and subgrade. Locations of “soft” track can cause increases in rail deflections and stresses, which increases the rate of rail deterioration. Traditionally, track modulus is measured onsite using static deflection testing where a known load is applied to the track, the resulting deflection is measured, and this deflection is extrapolated to the track modulus. However, this is time consuming and labor-intensive, especially if measurements are to be taken at multiple points along the track. To address the challenges with track modulus measurement, this research attempts to refine existing methods and expand the scale of monitoring by leveraging data from railcars to identify problem locations on the track and relate measured responses to specific track deficiencies. The approach allows making continuous estimations over long sections of the track and also is less costly, as self-contained acceleration and data acquisition systems are inexpensive and easy to attach to the vehicle bodies. This research will use technologies such as the ground penetrating radar (GPR) and track geometry cars in combination with low-cost sensors placed on the existing plant of rolling stock for accelerated track monitoring. Track response to track conditions will be measured. Constitutive load-deflection relationships will be established between track condition, loading, and deflection to determine the track modulus. The modulus will be characterized by leveraging the mechanics that relates vehicle accelerations to the condition of track upon which the instrumented vehicle travels. The research team is experienced in the use of low-cost accelerometers for bridge monitoring and assessment, where it established the mechanistic relationships between loading and response under various conditions and showed the feasibility of determining quantifiable, specific conditions from the acceleration data. Using this expertise and experience, the team now seeks to develop mechanics-based relationships that correlate railcar body acceleration profiles to track behavior and, eventually, track condition. This project is the next step towards full implementation, where it will use previously characterized conditions on a model test to support the constitutive load-deflection relationships. The overall impact of this work will be the widespread monitoring of track infrastructure through the installation of low-cost accelerometers on existing rollingstock. The industrial partner, BNSF, will provide support and help with implementation.