Utilization of Genetic Programming to Establish Demand Forecast in Taiwan International Flights   [GP] [DF] [TIF]

by

Yang, H.-H., Chen, S.-H., Hung, J.-Y., Hung, C.-T. and Chung, M.-L.

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Info: 2nd International Conference on Information Engineering and Computer Science (ICIECS), 2010 (Conference proceedings), 2010
Keywords:genetic algorithms, genetic programming, Taiwan international flights, airlines, demand forecast, mean absolute percent error, demand forecasting, travel industry
Abstract:
Accurately prediction is the most important way to cost down for airlines. The study was focused on build up forecast model of five Taiwan international flights [TIF] included Bali, Bang Kong, Ho Chi Minh City, Kuala Lumpur, and Singapore. Genetic programming [GP] was adopted to establish simulation models, [SM] and Mean Absolute Percent Error (MAPE) also was used to evaluate the performance of those models. The ten years of historical passenger's data was collected and analysis, and finally the demand forecast [DF] of five flights in 2010 would be conducted. The validations MAPE of models were lower than 10percent expect Bali flights. Based on experience of traditional statistic method included linear regression [LR] and time series, [TS] the ability of Genetic programming models [GP] were excellent. The forecast error of Bali flights were 11percent and it may be caused by a series accident. On the basis of above results, Genetic programming [GP] could be the feasible approach for prediction of five flights in Taiwan. In addition, the passengers to Singapore would substantially increase in 2010-2011, and the issue is worthy to further study for airlines and government.
Notes:
Also known as cite{5677766}
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BibTex:
@InProceedings{Yang:2010:ICIECS,
  author =       "Hui-Hua Yang and Shih-Huang Chen and Jui-Ying Hung and
                 Ching-Tsung Hung and Meng-Lung Chung",
  title =        "Utilization of Genetic Programming to Establish Demand
                 Forecast in Taiwan International Flights",
  booktitle =    "2nd International Conference on Information
                 Engineering and Computer Science (ICIECS), 2010",
  year =         "2010",
  month =        "25-26 " # dec,
  abstract =     "Accurately prediction is the most important way to
                 cost down for airlines. The study was focused on build
                 up forecast model of five Taiwan international flights
                 included Bali, Bang Kong, Ho Chi Minh City, Kuala
                 Lumpur, and Singapore. Genetic programming was adopted
                 to establish simulation models, and Mean Absolute
                 Percent Error (MAPE) also was used to evaluate the
                 performance of those models. The ten years of
                 historical passenger's data was collected and analysis,
                 and finally the demand forecast of five flights in 2010
                 would be conducted. The validations MAPE of models were
                 lower than 10percent expect Bali flights. Based on
                 experience of traditional statistic method included
                 linear regression and time series, the ability of
                 Genetic programming models were excellent. The forecast
                 error of Bali flights were 11percent and it may be
                 caused by a series accident. On the basis of above
                 results, Genetic programming could be the feasible
                 approach for prediction of five flights in Taiwan. In
                 addition, the passengers to Singapore would
                 substantially increase in 2010-2011, and the issue is
                 worthy to further study for airlines and government.",
  keywords =     "genetic algorithms, genetic programming, Taiwan
                 international flights, airlines, demand forecast, mean
                 absolute percent error, demand forecasting, travel
                 industry",
  doi =          "doi:10.1109/ICIECS.2010.5677766",
  ISSN =         "2156-7379",
  notes =        "Also known as cite{5677766}",
}