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Air passenger flow forecasting using nonadditive forecast combination with grey prediction

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  • Hu, Yi-Chung

Abstract

Air passenger flow forecasting plays a critical role in managing air transportation. It is important to develop accurate methods for forecasting the flow of passengers to formulate relevant operational plans and the schedule of airports. Even though empirical evidence has shown that forecast combination can improve the prediction accuracy of single-model forecasts, no study in the literature on transportation to date has examined the performance of such combinations to predict the demand for air transport. In light of the usefulness of grey prediction, which does not require applying any statistical test to examine the data at hand, and relationships among forecasts to be reckoned with, this study proposes using the nonadditive Choquet fuzzy integral to nonlinearly synthesize forecasts from four commonly used univariate grey prediction models into composite forecasts. The results of experiments confirmed that the risk arising from the incorrect choice of forecasting models can be eliminated by using forecast combinations. The proposed combination method based on grey prediction significantly outperformed the other competitors considered.

Suggested Citation

  • Hu, Yi-Chung, 2023. "Air passenger flow forecasting using nonadditive forecast combination with grey prediction," Journal of Air Transport Management, Elsevier, vol. 112(C).
    • Handle: RePEc:eee:jaitra:v:112:y:2023:i:c:s0969699723000820
    DOI: 10.1016/j.jairtraman.2023.102439
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