Asia-Pacific Journal of Business (아태비즈니스연구)

KNU The Institute of Management & Economy Research (강원대학교 경영경제연구소)
권호 표지
DOI QR코드

DOI QR Code

Forecasting short-term transportation demand at Gangchon Station in Chuncheon-si using time series model

시계열모형을 활용한 춘천시 강촌역 단기수송수요 예측

  • Chang-Young Jeon (Department of Tourism Administration, Kangwon National University) ;
  • Jia-Qi Liu (Department of Tourism Administration, Kangwon National University) ;
  • Hee-Won Yang (Department of Innovative Growth Research, Research Institute for Gangwon)
  • 전창영 (강원대학교 관광경영학과 ) ;
  • 유가기 (강원대학교 관광경영학과) ;
  • 양희원 (강원연구원 혁신성장실)
  • Received : 2023.11.30
  • Accepted : 2023.12.22
  • Published : 2023.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

Purpose - This study attempted to predict short-term transportation demand using trains and getting off at Gangchon Station. Through this, we present numerical data necessary for future tourist inflow policies in the Gangchon area of Chuncheon and present related implications. Design/methodology/approach - This study collected and analyzed transportation demand data from Gangchon Station using the Gyeongchun Line and ITX-Cheongchun Train from January 2014 to August 2023. Winters exponential smoothing model and ARIMA model were used to reflect the trend and seasonality of the raw data. Findings - First, transportation demand using trains to get off at Gangchon Station in Chuncheon City is expected to show a continuous increase from 2020 until the forecast period is 2024. Second, the number of passengers getting off at Gangchon Station was found to be highest in May and October. Research implications or Originality - As transportation networks are improving nationwide and people's leisure culture is changing, the number of tourists visiting the Gangchon area in Chuncheon City is continuously decreasing. Therefore, in this study, a time series model was used to predict short-term transportation demand alighting at Gangchon Station. In order to calculate more accurate forecasts, we compared models to find an appropriate model and presented forecasts.

Keywords

References

  1. 김경범.황경수 (2012). "계절 ARIMA 모형을 이용한 제주공항 여객 수요예측 및 효율적 운영에 관한 연구", 한국산학기술학회 논문지, 13(8), 3381-3388.
  2. 김관형‧김한수 (2011). "개입 ARIMA모형을 이용한 KTX 수요예측", 한국철도학회 논문집, 14(5), 470-476.
  3. 김동규 (2020). "시계열 자료를 활용한 도시철도 수요 예측", Journal of The Korean Data Analysis Society, 22(2), 753-765. https://doi.org/10.37727/jkdas.2020.22.2.753
  4. 김동규.김한영 (2019). "시계열분석 기반 대구 도시철도 수요 예측", 의사결정학연구, 27(1), 1-11.
  5. 김범승 (2014). "계절 ARIMA 모형을 이용한 여객수송수요 예측: 중앙선을 중심으로.", 한국철도학회 논문집, 17(4), 307-312.
  6. 김상원 (2011). "윈터스 지수평활법을 이용한 제주도 관광객 수요예측", 한국지역경제연구, 19, 133-154.
  7. 김상원 (2018). "제주특별자치도 관광수요 예측모델의 정확도 비교", 동북아관광연구, 14(2), 219-237.
  8. 김상원.박미선 (2016). "수원시 관광객 수요예측 모델의 정확도 비교", 동북아관광연구, 12(4), 121-142.
  9. 김효종.정찬묵 (2010). "철도수요예측을 위한 직접수요모형 개발에 관한 연구", 한국철도학회 학술발표대회 논문집,, 2166-2178.
  10. 노윤승.도명식 (2015). "SARIMA 모형을 이용한 철도여객 단기수송수요 예측", 한국 ITS 학회 논문지, 14(4), 18-26.
  11. 박득희‧이계희‧이민정 (2015). "방한 중국 관광객 수요예측 모델의 비교 연구", 관광연구저널, 29(3), 29-40.
  12. 박득희.강상훈.계희 (2020). "지속가능한 관광성장을 위한 방한 일본인 관광객 수요예측: 시계열 계량 모형 적용", 관광연구저널, 34(3), 47-60.
  13. 방성철‧박광서 (2022). "지수평활법과 ARIMA 모형을 이용한 무역인력 수급예측에 관한 연구", 무역상무연구, 93, 177-196.
  14. 송근석.이충기 (2007). "관광수요 예측모형의 선정 시 표본 후 예측의 중요성", 관광연구, 22(2), 95-116.
  15. 송근석.이충기 (2009). "결합기법을 이용한 관광수요예측", 관광레저연구, 21(1), 183-202.
  16. 이충기(2011). 관광응용경제학. 일신사
  17. 최가영.이정희.유리화 (2017). "시계열분석을 통한 자연휴양림 계절별 이용수요 예측: 계절 ARIMA 모형과 지수평활 모형을 중심으로", 관광경영연구, 76, 271-289.
  18. Bi, J. W., Y. Liu and H. Li (2020). Daily tourism volume forecasting for tourist attractions. Annals of Tourism Research, 83, 102923.
  19. Box, G. E., G. M. Jenkins, G. C. Reinsel and G. M. Ljung (2015). Time series analysis: forecasting and control. John Wiley and Sons.
  20. Ljung, G. M. and G. E. Box (1979). The likelihood function of stationary autoregressive-moving average models. Biometrika, 66(2), 265-270. https://doi.org/10.1093/biomet/66.2.265
  21. Makridakis, S., S. C. Wheelwright and V. E. McGee (1983). The Delphi approach. Forecasting Methods and Applications, 2nd ed., Wiley, New York, 652-655.
  22. Palmer, A., J. J. Montano and A. Sese (2006). Designing an artificial neural network for forecasting tourism time series. Tourism management, 27(5), 781-790. https://doi.org/10.1016/j.tourman.2005.05.006
  23. Pan, B., and Y. Yang (2017). Forecasting destination weekly hotel occupancy with big data. Journal of travel research, 56(7), 957-970. https://doi.org/10.1177/0047287516669050
  24. Silva, E. S., H. Hassani, S. Heravi and X. Huang (2019). Forecasting tourism demand with denoised neural networks. Annals of Tourism Research, 74, 134-154. https://doi.org/10.1016/j.annals.2018.11.006
  25. Vatsa, P. (2021). Seasonality and cycles in tourism demand-redux. Annals of Tourism Research, 90, 103105.
  26. Zhang, Y., G. Li, B. Muskat, and R. Law (2021). Tourism demand forecasting: A decomposed deep learning approach. Journal of Travel Research, 60(5), 981-997. https://doi.org/10.1177/0047287520919522