Surface congestion leads to significant increases in taxi times and fuel burn at major airports. In this paper, we formulate the airport surface congestion management problem as a dynamic control problem. We address two main challenges: the random delay between actuation (at the gate) and the server being controlled (the runway), and the need to develop control strategies that can be implemented in practice by human air traffic controllers. The second requirement necessitates a strategy that periodically updates the rate that departures pushback from their gates. We model the runway system as a semi-Markov process using surface surveillance data. We use this modeling framework to derive optimal pushback policies to control congestion. Finally, we present the results of the real-world implementation and field testing of this control protocol at Boston Logan International Airport.
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