For informed and more cost-effective maintenance and rehabilitation (M&R) needs assessments, the structural and surface conditions should be incorporated into the pavement management decision-making processes. The desire to characterize the network-level structural conditions in recent years has led to research efforts to investigate, validate, and demonstrate the effectiveness of Traffic Speed Deflectometer Devices (TSDDs). Several algorithms exist to provide the network-level and project-level information. However, none of them have considered the uncertainty of the data collected measurements related to the type and stiffness of the pavement type, and related to the TSD hardware and software. This study aims to identify robust indices for network and project-level applications and best-suited procedures for implementing them based on the type of pavement. The goals of this project are to provide guidelines and define processes to maximize the information and minimize the cost of network- and project-level uses of TSDDs. The results of this study will be of particular value to SHAs to maximize their benefit-cost-ratio of using TSDDs by avoiding data collection on sections that are outside the useful range of operation of TSD and use the best algorithm to analyze the data collected that balances the uncertainties in the measurements and analysis.