Journal of the Korean Society for Aviation and Aeronautics (한국항공운항학회지)

The Korean Society for Aviation and Aeronautics (한국항공운항학회)
권호 표지
DOI QR코드

DOI QR Code

A Study on the Air Travel Demand Forecasting using time series ARIMA-Intervention Model

ARIMA-Intervention 시계열모형을 활용한 제주 국내선 항공여객수요 추정

  • 김민수 (한국항공대학교 대학원 경영학과) ;
  • 김기웅 (한국항공대학교 경영학과) ;
  • 박성식 (한국항공대학교 대학원 경영학과)
  • Received : 2012.02.28
  • Accepted : 2012.03.13
  • Published : 2012.03.31
Download PDF
⟨ Previous Next ⟩

Abstract

The purpose of this study is to analyze the effect of intervention variables which may affect the air travel demand for Jeju domestic flights and to anticipate the air travel demand for Jeju domestic flights. The air travel demand forecasts for Jeju domestic flights are conducted through ARIMA-Intervention Model selecting five intervention variables such as 2002 World Cup games, SARS, novel swine-origin influenza A, Yeonpyeongdo bombardment and Japan big earthquake. The result revealed that the risk factor such as the threat of war that is a negative intervention incident and occurred in Korea has the negative impact on the air travel demand due to the response of risk aversion by users. However, when local natural disasters (earthquakes, etc) occurring in neighboring courtiers and global outbreak of an epidemic gave the negligible impact to Korea, negative intervention incident would have a positive impact on air travel demand as a response to find alternative due to rational expectation of air travel customers. Also we realize that a mega-event such as the 2002 Korea-Japan World Cup games reduced the air travel demand in a short-term period unlike the perception in which it will increase the air travel demand and travel demands in the corresponding area.

Keywords

References

  1. 강관보(2008), 제주지역 항공노선의 항공요금 저감방안, 제주발전연구원.
  2. 곽우심(2006), 항공여객수요 예측에 대한 실증연구, 한국항공대학교 대학원 석사학위논문.
  3. 김병종․이민희(2008), 부산권 항공수요 예측 연구, 한국항공운항학회지, 16(1) : 46-57
  4. 박상곤(2004), 테러가 관광에 미치는 영향 분석: 미국 9․11테러를 중심으로, 관광학연구, 28(2): 77-94
  5. 안경모․이광우(2005), ARIMA Intervention Model을 이용한 한국인 관광객의 태국여행수요 예측에 관한 연구, 한국호텔경영학회, 14(4) : 273-288
  6. 우경(2002), 개입-ARIMA모형을 이용한 지가 변동 예측에 관한 연구: 지역별 하부 토지시장을 중심으로, 국토연구, 30(0): 51-64
  7. 윤석홍․김맹선(2005). 관광목적여행수요예측의 계량경제모형, 국제지역연구 9(2) : 553-570
  8. 윤석홍․최승회(2006). 북한의 테러가 항공수요에 미치는 영향, 국제지역연구10(1) : 40-53
  9. 이덕기(1999), 예측방법의 이해, SPSS아카데미
  10. 이덕기(2002), 마케팅을 위한 예측과 시나리오 분석, 데이타솔루션
  11. 이원우(2009), 예측을 위한 통계적 기법, 자유아카데미
  12. 이종원(1998), 계량경제학, 박영사
  13. 이충기․송학준(2007), 최적 시계열 수요예측 모델선정에 관한 연구, 관광학연구, 31(6): 289-311
  14. 이휘영․윤문길(2008), 수익경영을 위한 항공수요 예측에 관한 연구, 한국항공경영학회지 6(1): 59-71
  15. 정동빈(2009), SPSS 시계열수요예측 I, 한나래아카데미
  16. 정동빈(2009), SPSS 시계열수요예측 II, 한나래아카데미
  17. 제갈돈․송건섭(1998), 간여시계열분석을 이용한 대구시 114유료화정책에 대한 응답비용 효과, 한국데이터정보과학회지, 9(2): 139-147
  18. 최휴종(2007), 항공 여객수요의 신예측모형, 관광경영학연구, 32(0) : 125-147
  19. 한국공항공사, www.airport.co.kr
  20. 허희영(2005), 중형기 국내수요예측에 대한 연구, 한국항공대학교 경영연구소
  21. 홍미영(2010), 교통수단에 따른 외래관광 수요함수 결정요인. 세종대학교 박사학위 청구논문
  22. 홍미영․임은순(2010), 교통수단에 따른 해외관광수요 결정요인 분석. 대한관광경영학회, 25(2): 179-195
  23. Box, G.E.P & Jenkins, G.M.(1976), Time series analysis : Forecasting and control, 2nd ed. sanFrancisco:Holden-Day
  24. Box, G.E.P & Jenkins, G.M. & G.C. Reinsel(1994), Time Series Anlaysis: Forecasting and Control, 3rd ed. New Jersey: Prentice Hall
  25. Box, G.E.P & Tiao, G.C(1975), Intervention Analysis with Application to Economic and Environmental Problems, Journal of American Statistical Association, Vol70. 70-79 https://doi.org/10.1080/01621459.1975.10480264
  26. Cambell, D.T & Stanley, J.C(1966), Experimental and Quasi-Experimential Design for Research, Chicago: Rand McNally
  27. Chatfield, C.(2004), The analysis of time series, an introduction, 6th edition:New York, Chapman & Hall/CRC
  28. Enders, W.(1995), Applied econometric time series (1st ed), New York: John Wiley & Sons
  29. Glass, G.V.(1972), Estimating the effects of intervention into a nonstationary time series, American Educational Research Journal, Vol9. 463-477. https://doi.org/10.3102/00028312009003463
  30. Goh, B.H(2005), The dynamic effects of the Asian Financial crisis on construction demand and tender price levels Singapore. Building and Enviroment, 40(2): 267-276 https://doi.org/10.1016/j.buildenv.2004.07.012
  31. Lewis, C.D.(1982), Industrial and Business Forecasting Method, London: Butterworth.
  32. Nelson, J.P(2022), Consumer Bankruptcies and bankruptcy reform act: A time series intervention analysis 1960-1997. Journal of Financial Services Research, 17(2): 181-200
  33. Quayson, J. & Var, T.(1982), A tourism demand function for the Okanagan, BC. Tourism Management, 3(2), 108-115. https://doi.org/10.1016/0261-5177(82)90006-1
  34. R. Stein & P. Shaman(1989), A fixed point characterization for bias of auto -regressive estimators, The Analysis of Statistics 17, no.3, 1275-1284
  35. S. Crunk(1999), Dissertation on tapering to improve yule-walker estimation in autoregressive processes
  36. Wold H.(1938), A Study in the Analysis of Stationary Time Series, Almgrist & Wiksell, Stockholm
  37. Z. Ismail & Suhartono & A.Yahaya(2009), Intervention Model for Analyzing the Impact of Terrorism to Tourism Industry. Journey of Mathematics 5(4): 322-329

Cited by

  1. A Study on Fare Rearrangement of Coastwise Passenger Transport after the Disaster of the Sewol Ferry - Introduction of Bunkering Surcharge - vol.32, pp.3, 2012, https://doi.org/10.37059/tjosal.2016.32.3.457
  2. Analysis of Tariff Policy of Coastal Passenger Shipping in Korea vol.33, pp.1, 2012, https://doi.org/10.37059/tjosal.2017.33.1.179