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Hybrid feedforward ANN with NLS-based regression curve fitting for US air traffic forecasting

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Abstract

Due to the rapid growth of the number of passengers over the few recent decades, air traffic forecasting has become a crucial tool for digital transportation systems, playing a fundamental role in the planning and development of traffic management and control systems. The main goal of forecasting in air transport is to predict traffic conditions in a network, on the basis of its past behavior, in order to improve safety and reduce airspace congestion. Nevertheless, air traffic time series often present an intricate behavior because of their irregular trends and strong seasonalities. In this paper, the methodology based on time series decomposition and artificial neural networks (ANNs) is thus reviewed and reconsidered within this framework of air traffic management. In this respect, a hybrid approach coupling feedforward neural networks with a nonlinear least squares-based regression curve fitting is developed for the multistep-ahead prediction. Empirical experiments are conducted in order to demonstrate the effectiveness of the proposed model on passenger traffic real datasets. The results show that, despite its simplicity, the base model is capable of generating accurate forecasts, with a performance comparable with that of powerful state-of-the-art forecasting models. In addition, there is evidence that trend pretreatment (wholly or partially) would rather degrade the forecasting accuracy.

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Notes

  1. In computational networks, an activation function is a smooth and symmetrically nonlinear function. The domain of outputs of a FF is governed by an activation function. An activation function is sometimes assumed an identity function.

  2. https://www.bts.gov.

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Acknowledgements

This work was supported by the Deanship of Scientific Research (DSR), King Abdulaziz University, Jeddah, under grant No. (DF-289-130-1441). The authors, therefore, gratefully acknowledge DSR technical and financial support.

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Authors and Affiliations

  1. Department of Statistics, Faculty of Sciences, King Abdulaziz University, PO BOX 80203, Jeddah, 21589, Saudi Arabia

    Foued Saâdaoui

  2. Laboratoire d’Algèbre, Théorie de Nombres et Analyse Non-linéaire, Faculté des Sciences, University of Monastir, 5019, Monastir, Tunisia

    Hayet Saadaoui & Hana Rabbouch

  3. LAMETA, INRA, 2 Place Viala, Bâtiment 26, 34060, Montpellier, France

    Hayet Saadaoui

  4. Institut Supérieur de Gestion de Tunis, Université de Tunis, Cité Bouchoucha, 2000, Tunis, Tunisia

    Hana Rabbouch

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  1. Foued Saâdaoui
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Correspondence to Foued Saâdaoui.

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Saâdaoui, F., Saadaoui, H. & Rabbouch, H. Hybrid feedforward ANN with NLS-based regression curve fitting for US air traffic forecasting. Neural Comput & Applic 32, 10073–10085 (2020). https://doi.org/10.1007/s00521-019-04539-5

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  • DOI: https://doi.org/10.1007/s00521-019-04539-5

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