Damp trend Grey Model forecasting method for airline industry

  • Authors:

  • Rafael Bernardo Carmona BeníTez;Rafael Bernardo Carmona Paredes;Gabriel Lodewijks;Joao Lemos Nabais

  • Affiliations:

  • Universidad Anáhuac México Norte, School of Business and Economics, Av. Universidad Anahuac No. 46, Col. Lomas Anáhuac, Huixquilucan, 52786 State of Mexico, Mexico;Universidad Nacional Autónoma de México (UNAM), Institute of Engineering, Mechanical Engineering, Torre de Ingenieria Piso 2 ala Norte, Ciudad Universitaria, 04510 Mexico City, Mexico;Delft University of Technology (TUDELFT), Department of Transport Engineering and Logistics, Mekelweg 2, 2628CD Delft, The Netherlands;IDMEC, Department of Informatics and Systems Engineering, Setubal School of Technology, Polytechnic Institute of Setubal, Setubal, Portugal

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2013

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Abstract

This paper presents a modification of the Grey Model (GM) to forecast routes passenger demand growth in the air transportation industry. Forecast methods like Holt-Winters, autoregressive models, exponential smoothing, neural network, fuzzy logic, GM model calculate very high airlines routes pax growth. For this reason, a modification has been done to the GM model to damp trend calculations as time grows. The simulation results show that the modified GM model reduces the model exponential estimations grow. It allows the GM model to forecast reasonable routes passenger demand for long lead-times forecasts. It makes this model an option to calculate airlines routes pax flow when few data points are available. The United States domestic air transport market data are used to compare the performance of the GM model with the proposed model.