This thesis investigates how the COVID-19 pandemic has reshaped public transit ridership in Philadelphia and evaluates the extent to which Negative Binomial Distribution models (NBDs) can be used to track and forecast ridership in its aftermath. It tests several approaches for modeling pandemic-related disruption—including binary, count, and tapering multiplier specifications—and assesses their performance across both route- and mode-level datasets. Results suggest that NBDs are well-suited to managing overdispersed ridership data and capturing hierarchical variation, though with limited predictive precision in post-pandemic years. Among the tested specifications, the binary COVID indicator most effectively captured the persistent nature of ridership decline, while the tapering multiplier rarely indicated meaningful recovery. Route-level models offered finer spatial resolution, whereas mode-level models produced more stable estimates of system-wide trends. Together, these findings demonstrate the value and limits of using NBDs to quantify long-term shifts in transit usage, offering a foundation for more nuanced forecasting.