Likelihood-free parameter estimation for dynamic queueing networks: case study of passenger flow in an international airport terminal

TL;DR

This paper introduces a novel likelihood-free parameter estimation method for dynamic queueing networks, specifically applied to passenger flow in an airport terminal, enabling accurate modeling and decision support despite computational challenges.

Contribution

The paper develops the first efficient parameter inference approach for DQNs using ABC with maximum mean discrepancy, overcoming computational hurdles of traditional simulation methods.

Findings

Model accurately replicates passenger flow behaviour.

Method provides useful decision support tools.

Prediction intervals can be constructed from posterior samples.

Abstract

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.