Airports in the United States serve as critical transportation hubs, handling hundreds of millions of travelers each year. A major bottleneck in air travel is the TSA security checkpoint. For TSA directors, accurately forecasting passenger volume is essential for effective staffing and resource allocation. Reliable predictions of daily throughput can also help travelers anticipate longer-than-usual wait times and plan accordingly.

Objective: Develop a predictive model for passenger throughput at TSA checkpoints. We will begin with a single airport to evaluate feasibility and performance, forecasting passenger volume over multiple timescales (e.g., daily or weekly) and determining which horizon yields the most reliable results.

Deliverable: A model which can be trained to predict the daily passenger throughput at TSA checkpoints in major U.S. airports.

Data Source: TSA Hourly Passenger Throughput dataset (https://www.tsa.gov/foia/readingroom)