A low-cost vibration-monitoring system was developed and installed on an urban steel-plated stress-ribbon footbridge. The system continuously measures the acceleration [using 18 triaxial microelectromechanical system (MEMS) accelerometers distributed along the structure), the ambient temperature, and the wind velocity and direction. Automated output-only modal parameter estimation based on the stochastic subspace identification (SSI) was carried out to extract the modal parameters (i.e., the natural frequencies, damping ratios, and modal shapes). Thus, this study analyzed the time evolution of the modal parameters over data monitoring for 1 year. First, for similar environmental/operational factors, the uncertainties associated with the SSI-based techniques used and to the acceleration records used were studied and quantified. Second, a methodology for tracking the vibration modes was established, because several of them with closely spaced natural frequencies were identified. Third, the modal parameters were correlated against external factors. It has been shown that this stress-ribbon structure is highly sensitive to temperature variations (frequency changes of more than 20%) with strongly seasonal and daily trends. Fairly simple dynamic multiple regression models for the lowest persistent vibration modes were derived, and excellent correlations for some of them were obtained. These correlations enable the influence of these uncertainties on modal estimates to be removed, thus facilitating their use as damage-sensitive features.