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Spatiotemporal auto‐regressive model for origin–destination air passenger flows

Author

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  • Keunseo Kim
  • Vinnam Kim
  • Heeyoung Kim

Abstract

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.

Suggested Citation

  • Keunseo Kim & Vinnam Kim & Heeyoung Kim, 2019. "Spatiotemporal auto‐regressive model for origin–destination air passenger flows," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 182(3), pages 1003-1016, June.
    • Handle: RePEc:bla:jorssa:v:182:y:2019:i:3:p:1003-1016
    DOI: 10.1111/rssa.12427
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    Cited by:

    1. Oshan, Taylor M., 2020. "The spatial structure debate in spatial interaction modeling: 50 years on," OSF Preprints 42vxn, Center for Open Science.
    2. Wei Yu & Xiaofei Ye & Jun Chen & Xingchen Yan & Tao Wang, 2020. "Evaluation Indexes and Correlation Analysis of Origination–Destination Travel Time of Nanjing Metro Based on Complex Network Method," Sustainability, MDPI, vol. 12(3), pages 1-21, February.

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