  Many special events, including sport games and concerts, often cause surges
in demand and congestion for transit systems. Therefore, it is important for
transit providers to understand their impact on disruptions, delays, and fare
revenues. This paper proposes a suite of data-driven techniques that exploit
Automated Fare Collection (AFC) data for evaluating, anticipating, and managing
the performance of transit systems during recurring congestion peaks due to
special events. This includes an extensive analysis of ridership of the two
major stadiums in downtown Atlanta using rail data from the Metropolitan
Atlanta Rapid Transit Authority (MARTA). The paper first highlights the
ridership predictability at the aggregate level for each station on both event
and non-event days. It then presents an unsupervised machine-learning model to
cluster passengers and identify which train they are boarding. The model makes
it possible to evaluate system performance in terms of fundamental metrics such
as the passenger load per train and the wait times of riders. The paper also
presents linear regression and random forest models for predicting ridership
that are used in combination with historical throughput analysis to forecast
demand. Finally, simulations are performed that showcase the potential
improvements to wait times and demand matching by leveraging proposed
techniques to optimize train frequencies based on forecasted demand.
