  Dynamic queueing networks (DQN) model queueing systems where demand varies
strongly with time, such as airport terminals. With rapidly rising global air
passenger traffic placing increasing pressure on airport terminals, efficient
allocation of resources is more important than ever. Parameter inference and
quantification of uncertainty are key challenges for developing decision
support tools. The DQN likelihood function is, in general, intractable and
current approaches to simulation make likelihood-free parameter inference
methods, such as approximate Bayesian computation (ABC), infeasible since
simulating from these models is computationally expensive. By leveraging a
recent advance in computationally efficient queueing simulation, we develop the
first parameter inference approach for DQNs. We demonstrate our approach with
data of passenger flows in a real airport terminal, and we show that our model
accurately recreates the behaviour of the system and is useful for decision
support. Special care must be taken in developing the distance for ABC since
any useful output must vary with time. We use maximum mean discrepancy, a
metric on probability measures, as the distance function for ABC. Prediction
intervals of performance measures for decision support tools are easily
constructed using draws from posterior samples, which we demonstrate with a
scenario of a delayed flight.
