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Likelihood‐free parameter estimation for dynamic queueing networks : case study of passenger flow in an international airport terminal
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Ebert, Anthony, Dutta, Ritabrata, Mengersen, Kerrie, Mira, Antonietta, Ruggeri, Fabrizio and Wu, Paul (2021) Likelihood‐free parameter estimation for dynamic queueing networks : case study of passenger flow in an international airport terminal. Journal of the Royal Statistical Society: Series C (Applied Statistics), 70 (3). pp. 770-792. doi:10.1111/rssc.12487 ISSN 0035-9254.
An open access version can be found in:
Official URL: http://dx.doi.org/10.1111/rssc.12487
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.
Item Type: | Journal Article | ||||||||
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Statistics | ||||||||
SWORD Depositor: | Library Publications Router | ||||||||
Journal or Publication Title: | Journal of the Royal Statistical Society: Series C (Applied Statistics) | ||||||||
Publisher: | Wiley-Blackwell Publishing Ltd. | ||||||||
ISSN: | 0035-9254 | ||||||||
Official Date: | 4 June 2021 | ||||||||
Dates: |
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Volume: | 70 | ||||||||
Number: | 3 | ||||||||
Page Range: | pp. 770-792 | ||||||||
DOI: | 10.1111/rssc.12487 | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||||
Open Access Version: |
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