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A hybrid forecasting architecture for air passenger demand considering search engine data and spatial effect

Author

Listed:
  • Liang, Xiaozhen
  • Hong, Chenxi
  • Chen, Jiaqi
  • Wang, Yingying
  • Yang, Mingge

Abstract

As the global process of digitalization accelerates and breakthroughs in internet technology emerge, governments worldwide are advocating for data-driven decision-making, aiming to enhance public service efficiency and stimulate economic growth. Against this backdrop, this study focuses on utilizing search engine data (SED) to improve air passenger demand forecasting, responding to national policies aimed at enhancing data analysis capabilities and promoting the development of intelligent transportation systems; however, the existing research is confined to the exploration of the temporal dependency between SED and air passenger demand variables with ignoring the spatial dependency. In order to eliminate this blind spot and catch from various parts of tourist attention, this study proposes a novel SED-driven hybrid forecasting architecture inspired by the theory of spatial effect between adjacent tourist destinations. The architecture includes three main steps: (1) construction of spatial-temporal SED variables, based on two-stage data preprocessing method; (2) variable decomposition and reconstruction, based on TVF-EMD algorithm; (3) prediction of different components of air passenger demand, employed ARIMA model and IHGS-KELM based multi-model fusion strategy respectively, where the IHGS algorithm integrates the circle chaos initialization strategy and the nonlinear convergence factor strategy. To confirm the practical applicability of this hybrid architecture, five comparative experiments based on the actual dataset are designed. The principal results are concluded as follows: (1) spatial-temporal SED is conducive to a fairly accurate air passenger demand forecasting; (2) the multi-model fusion strategy can integrate the fortes of various types of prediction models to obtain better prediction accuracy; (3) the adaptive ensemble method based on IHGS-KELM can contribute to the upgradation of prediction performance of air passenger demand.

Suggested Citation

  • Liang, Xiaozhen & Hong, Chenxi & Chen, Jiaqi & Wang, Yingying & Yang, Mingge, 2024. "A hybrid forecasting architecture for air passenger demand considering search engine data and spatial effect," Journal of Air Transport Management, Elsevier, vol. 118(C).
    • Handle: RePEc:eee:jaitra:v:118:y:2024:i:c:s0969699724000760
    DOI: 10.1016/j.jairtraman.2024.102611
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