Utilization of genetic programming to establish demand forecast in Taiwan international flights

Hui Hua Yang, Shih Huang Chen, Jui Ying Hung, Ching Tsung Hung, Meng Lung Chung

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

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 10% 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 11% 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.

Original languageEnglish
Title of host publication2nd International Conference on Information Engineering and Computer Science - Proceedings, ICIECS 2010
DOIs
StatePublished - 2010
Event2nd International Conference on Information Engineering and Computer Science, ICIECS 2010 - Wuhan, China
Duration: 25 Dec 201026 Dec 2010

Publication series

Name2nd International Conference on Information Engineering and Computer Science - Proceedings, ICIECS 2010

Conference

Conference2nd International Conference on Information Engineering and Computer Science, ICIECS 2010
Country/TerritoryChina
CityWuhan
Period25/12/1026/12/10

Keywords

  • Airlines
  • Demand forecast
  • Genetic programming

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