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Spatiotemporal auto‐regressive model for origin–destination air passenger flows
Author(s) -
Kim Keunseo,
Kim Vinnam,
Kim Heeyoung
Publication year - 2019
Publication title -
journal of the royal statistical society: series a (statistics in society)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.103
H-Index - 84
eISSN - 1467-985X
pISSN - 0964-1998
DOI - 10.1111/rssa.12427
Subject(s) - aviation , air travel , scheduling (production processes) , computer science , autoregressive model , set (abstract data type) , meteorology , econometrics , geography , mathematics , mathematical optimization , aerospace engineering , engineering , programming language
Summary The growth of the global airline network has increased the importance of modelling origin–destination air passenger flows for better operational planning and scheduling. Origin–destination air passenger flows are correlated both spatially and temporally because of spatial and temporal relationships of human behaviours and environments. However, most existing studies for modelling air passenger flows have assumed that these relationships are independent; few studies have considered either spatial or temporal dependences. To consider both, we develop a spatiotemporal auto‐regressive model for monthly origin–destination air passenger flows. Benefitting from the special structure of a spatiotemporal dependence matrix, the model proposed can be extended to incorporate multi‐dimensional auto‐regressive coefficients for more flexible modelling. Its application to a real open access aviation data set demonstrates the effectiveness of the proposed model in forecasting monthly air passenger flows.