  We present a data-driven control framework for adaptively managing landside
congestion at airports. Ground traffic significantly impacts airport operations
and critical efficiency, environmental, and safety metrics. Our framework
models a real-world traffic intervention currently deployed at Seattle-Tacoma
International Airport (SEA), where a digital signboard recommends drivers to
divert to Departures or Arrivals depending on current congestion. We use
measured vehicle flow/speed and passenger volume data, as well as time-stamped
records of diversionary messages, to build a macroscopic system dynamics model.
We then design a model predictive controller that uses our estimated dynamics
to recommend diversions that optimize for overall congestion. Finally, we
evaluate our approach on 50 real-world historical scenarios at SEA where no
diversions were deployed despite significant congestion. Our results suggest
that our framework would have improved speed in the congested roadway by up to
three times and saved between 20 and 80 vehicle-hours of cumulative travel time
in every hour of deployment. Overall, our work emphasizes the opportunity of
algorithmic decision-making to augment operator judgment and intuition and
yield better real-world outcomes.
